Data Cleaning
1.
housing <- read.csv("housing.csv", stringsAsFactors = TRUE)
# remove unique identifier
housing <- housing[, -1]
# Factor variables that should be categorical not numerical
housing$OverallQual <- as.factor(housing$OverallQual)
housing$OverallCond <- as.factor(housing$OverallCond)
summary(housing)## MSSubClass MSZoning LotFrontage LotArea Street
## Min. : 20.0 C (all): 10 Min. : 21.00 Min. : 1300 Grvl: 6
## 1st Qu.: 20.0 FV : 65 1st Qu.: 59.00 1st Qu.: 7554 Pave:1454
## Median : 50.0 RH : 16 Median : 69.00 Median : 9478
## Mean : 56.9 RL :1151 Mean : 70.05 Mean : 10517
## 3rd Qu.: 70.0 RM : 218 3rd Qu.: 80.00 3rd Qu.: 11602
## Max. :190.0 Max. :313.00 Max. :215245
## NA's :259
## Alley LotShape LandContour Utilities LotConfig LandSlope
## Grvl: 50 IR1:484 Bnk: 63 AllPub:1459 Corner : 263 Gtl:1382
## Pave: 41 IR2: 41 HLS: 50 NoSeWa: 1 CulDSac: 94 Mod: 65
## NA's:1369 IR3: 10 Low: 36 FR2 : 47 Sev: 13
## Reg:925 Lvl:1311 FR3 : 4
## Inside :1052
##
##
## Neighborhood Condition1 Condition2 BldgType HouseStyle
## NAmes :225 Norm :1260 Norm :1445 1Fam :1220 1Story :726
## CollgCr:150 Feedr : 81 Feedr : 6 2fmCon: 31 2Story :445
## OldTown:113 Artery : 48 Artery : 2 Duplex: 52 1.5Fin :154
## Edwards:100 RRAn : 26 PosN : 2 Twnhs : 43 SLvl : 65
## Somerst: 86 PosN : 19 RRNn : 2 TwnhsE: 114 SFoyer : 37
## Gilbert: 79 RRAe : 11 PosA : 1 1.5Unf : 14
## (Other):707 (Other): 15 (Other): 2 (Other): 19
## OverallQual OverallCond YearBuilt YearRemodAdd RoofStyle
## 5 :397 5 :821 Min. :1872 Min. :1950 Flat : 13
## 6 :374 6 :252 1st Qu.:1954 1st Qu.:1967 Gable :1141
## 7 :319 7 :205 Median :1973 Median :1994 Gambrel: 11
## 8 :168 8 : 72 Mean :1971 Mean :1985 Hip : 286
## 4 :116 4 : 57 3rd Qu.:2000 3rd Qu.:2004 Mansard: 7
## 9 : 43 3 : 25 Max. :2010 Max. :2010 Shed : 2
## (Other): 43 (Other): 28
## RoofMatl Exterior1st Exterior2nd MasVnrType MasVnrArea
## CompShg:1434 VinylSd:515 VinylSd:504 BrkCmn : 15 Min. : 0.0
## Tar&Grv: 11 HdBoard:222 MetalSd:214 BrkFace:445 1st Qu.: 0.0
## WdShngl: 6 MetalSd:220 HdBoard:207 None :864 Median : 0.0
## WdShake: 5 Wd Sdng:206 Wd Sdng:197 Stone :128 Mean : 103.7
## ClyTile: 1 Plywood:108 Plywood:142 NA's : 8 3rd Qu.: 166.0
## Membran: 1 CemntBd: 61 CmentBd: 60 Max. :1600.0
## (Other): 2 (Other):128 (Other):136 NA's :8
## ExterQual ExterCond Foundation BsmtQual BsmtCond BsmtExposure
## Ex: 52 Ex: 3 BrkTil:146 Ex :121 Fa : 45 Av :221
## Fa: 14 Fa: 28 CBlock:634 Fa : 35 Gd : 65 Gd :134
## Gd:488 Gd: 146 PConc :647 Gd :618 Po : 2 Mn :114
## TA:906 Po: 1 Slab : 24 TA :649 TA :1311 No :953
## TA:1282 Stone : 6 NA's: 37 NA's: 37 NA's: 38
## Wood : 3
##
## BsmtFinType1 BsmtFinSF1 BsmtFinType2 BsmtFinSF2 BsmtUnfSF
## ALQ :220 Min. : 0.0 ALQ : 19 Min. : 0.00 Min. : 0.0
## BLQ :148 1st Qu.: 0.0 BLQ : 33 1st Qu.: 0.00 1st Qu.: 223.0
## GLQ :418 Median : 383.5 GLQ : 14 Median : 0.00 Median : 477.5
## LwQ : 74 Mean : 443.6 LwQ : 46 Mean : 46.55 Mean : 567.2
## Rec :133 3rd Qu.: 712.2 Rec : 54 3rd Qu.: 0.00 3rd Qu.: 808.0
## Unf :430 Max. :5644.0 Unf :1256 Max. :1474.00 Max. :2336.0
## NA's: 37 NA's: 38
## TotalBsmtSF Heating HeatingQC CentralAir Electrical X1stFlrSF
## Min. : 0.0 Floor: 1 Ex:741 N: 95 FuseA: 94 Min. : 334
## 1st Qu.: 795.8 GasA :1428 Fa: 49 Y:1365 FuseF: 27 1st Qu.: 882
## Median : 991.5 GasW : 18 Gd:241 FuseP: 3 Median :1087
## Mean :1057.4 Grav : 7 Po: 1 Mix : 1 Mean :1163
## 3rd Qu.:1298.2 OthW : 2 TA:428 SBrkr:1334 3rd Qu.:1391
## Max. :6110.0 Wall : 4 NA's : 1 Max. :4692
##
## X2ndFlrSF LowQualFinSF GrLivArea BsmtFullBath
## Min. : 0 Min. : 0.000 Min. : 334 Min. :0.0000
## 1st Qu.: 0 1st Qu.: 0.000 1st Qu.:1130 1st Qu.:0.0000
## Median : 0 Median : 0.000 Median :1464 Median :0.0000
## Mean : 347 Mean : 5.845 Mean :1515 Mean :0.4253
## 3rd Qu.: 728 3rd Qu.: 0.000 3rd Qu.:1777 3rd Qu.:1.0000
## Max. :2065 Max. :572.000 Max. :5642 Max. :3.0000
##
## BsmtHalfBath FullBath HalfBath BedroomAbvGr
## Min. :0.00000 Min. :0.000 Min. :0.0000 Min. :0.000
## 1st Qu.:0.00000 1st Qu.:1.000 1st Qu.:0.0000 1st Qu.:2.000
## Median :0.00000 Median :2.000 Median :0.0000 Median :3.000
## Mean :0.05753 Mean :1.565 Mean :0.3829 Mean :2.866
## 3rd Qu.:0.00000 3rd Qu.:2.000 3rd Qu.:1.0000 3rd Qu.:3.000
## Max. :2.00000 Max. :3.000 Max. :2.0000 Max. :8.000
##
## KitchenAbvGr KitchenQual TotRmsAbvGrd Functional Fireplaces
## Min. :0.000 Ex:100 Min. : 2.000 Maj1: 14 Min. :0.000
## 1st Qu.:1.000 Fa: 39 1st Qu.: 5.000 Maj2: 5 1st Qu.:0.000
## Median :1.000 Gd:586 Median : 6.000 Min1: 31 Median :1.000
## Mean :1.047 TA:735 Mean : 6.518 Min2: 34 Mean :0.613
## 3rd Qu.:1.000 3rd Qu.: 7.000 Mod : 15 3rd Qu.:1.000
## Max. :3.000 Max. :14.000 Sev : 1 Max. :3.000
## Typ :1360
## FireplaceQu GarageType GarageYrBlt GarageFinish GarageCars
## Ex : 24 2Types : 6 Min. :1900 Fin :352 Min. :0.000
## Fa : 33 Attchd :870 1st Qu.:1961 RFn :422 1st Qu.:1.000
## Gd :380 Basment: 19 Median :1980 Unf :605 Median :2.000
## Po : 20 BuiltIn: 88 Mean :1979 NA's: 81 Mean :1.767
## TA :313 CarPort: 9 3rd Qu.:2002 3rd Qu.:2.000
## NA's:690 Detchd :387 Max. :2010 Max. :4.000
## NA's : 81 NA's :81
## GarageArea GarageQual GarageCond PavedDrive WoodDeckSF
## Min. : 0.0 Ex : 3 Ex : 2 N: 90 Min. : 0.00
## 1st Qu.: 334.5 Fa : 48 Fa : 35 P: 30 1st Qu.: 0.00
## Median : 480.0 Gd : 14 Gd : 9 Y:1340 Median : 0.00
## Mean : 473.0 Po : 3 Po : 7 Mean : 94.24
## 3rd Qu.: 576.0 TA :1311 TA :1326 3rd Qu.:168.00
## Max. :1418.0 NA's: 81 NA's: 81 Max. :857.00
##
## OpenPorchSF EnclosedPorch X3SsnPorch ScreenPorch
## Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.00
## 1st Qu.: 0.00 1st Qu.: 0.00 1st Qu.: 0.00 1st Qu.: 0.00
## Median : 25.00 Median : 0.00 Median : 0.00 Median : 0.00
## Mean : 46.66 Mean : 21.95 Mean : 3.41 Mean : 15.06
## 3rd Qu.: 68.00 3rd Qu.: 0.00 3rd Qu.: 0.00 3rd Qu.: 0.00
## Max. :547.00 Max. :552.00 Max. :508.00 Max. :480.00
##
## PoolArea PoolQC Fence MiscFeature MiscVal
## Min. : 0.000 Ex : 2 GdPrv: 59 Gar2: 2 Min. : 0.00
## 1st Qu.: 0.000 Fa : 2 GdWo : 54 Othr: 2 1st Qu.: 0.00
## Median : 0.000 Gd : 3 MnPrv: 157 Shed: 49 Median : 0.00
## Mean : 2.759 NA's:1453 MnWw : 11 TenC: 1 Mean : 43.49
## 3rd Qu.: 0.000 NA's :1179 NA's:1406 3rd Qu.: 0.00
## Max. :738.000 Max. :15500.00
##
## MoSold YrSold SaleType SaleCondition SalePrice
## Min. : 1.000 Min. :2006 WD :1267 Abnorml: 101 Min. : 34900
## 1st Qu.: 5.000 1st Qu.:2007 New : 122 AdjLand: 4 1st Qu.:129975
## Median : 6.000 Median :2008 COD : 43 Alloca : 12 Median :163000
## Mean : 6.322 Mean :2008 ConLD : 9 Family : 20 Mean :180921
## 3rd Qu.: 8.000 3rd Qu.:2009 ConLI : 5 Normal :1198 3rd Qu.:214000
## Max. :12.000 Max. :2010 ConLw : 5 Partial: 125 Max. :755000
## (Other): 9After removing: ID and changing OverallCond & OverallQual to factor variables Categorical Variables: 45 Numerical Variables: 35
2.
# find and count NA's
sapply(housing, function(x) sum(is.na(x)))## MSSubClass MSZoning LotFrontage LotArea Street
## 0 0 259 0 0
## Alley LotShape LandContour Utilities LotConfig
## 1369 0 0 0 0
## LandSlope Neighborhood Condition1 Condition2 BldgType
## 0 0 0 0 0
## HouseStyle OverallQual OverallCond YearBuilt YearRemodAdd
## 0 0 0 0 0
## RoofStyle RoofMatl Exterior1st Exterior2nd MasVnrType
## 0 0 0 0 8
## MasVnrArea ExterQual ExterCond Foundation BsmtQual
## 8 0 0 0 37
## BsmtCond BsmtExposure BsmtFinType1 BsmtFinSF1 BsmtFinType2
## 37 38 37 0 38
## BsmtFinSF2 BsmtUnfSF TotalBsmtSF Heating HeatingQC
## 0 0 0 0 0
## CentralAir Electrical X1stFlrSF X2ndFlrSF LowQualFinSF
## 0 1 0 0 0
## GrLivArea BsmtFullBath BsmtHalfBath FullBath HalfBath
## 0 0 0 0 0
## BedroomAbvGr KitchenAbvGr KitchenQual TotRmsAbvGrd Functional
## 0 0 0 0 0
## Fireplaces FireplaceQu GarageType GarageYrBlt GarageFinish
## 0 690 81 81 81
## GarageCars GarageArea GarageQual GarageCond PavedDrive
## 0 0 81 81 0
## WoodDeckSF OpenPorchSF EnclosedPorch X3SsnPorch ScreenPorch
## 0 0 0 0 0
## PoolArea PoolQC Fence MiscFeature MiscVal
## 0 1453 1179 1406 0
## MoSold YrSold SaleType SaleCondition SalePrice
## 0 0 0 0 0# Outlier detection in SalePrice
summary(housing$SalePrice)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 34900 129975 163000 180921 214000 755000IQR = 214000 - 129975
lower.bound = 129975 - 1.5*IQR
upper.bound = 214000 + 1.5*IQRVariables with missing values: LotFrontage, Alley, MasVnrType, MasVnrArea, BsmtQual, BsmtCond, BsmtExposre, BsmtFinType1, BsmtFinType2, Electrical, FireplaceQu, GarageType, GarageYrBlt, GarageFinish, GarageQual, GarageCond, PoolQC, Fence, MiscFeature
Although there is a large list of variables with missing data, some of the variables that are NA’s are not “really” missing. For example we see the Garage variables all have 81 missing values, well, I think it is safe to assume that there are 81 properties that do not have garages, rather than the data actually missing. The same can be inferred for nearly all the “missing” data, though a closer look at some of the variables should be assessed before the next step. These missing variables will probaby be best handled by taking the numeric values to a value of zero, if it is determined that the home most likely doesn’t contain the feature rather than it being truly missing.
While using the IQR range to detect outliers shows quite a few outliers, looking at the data tells another story. The highest sales priced seem to be correlated with at grade living living space and finished basement living space as well as age of home and other amenities, this would suggest that the prices are reflective of the data and more importantly, important to the data to depict an accurate model. I have determined that removing any data points as outliers of Sales Price would be more detrimental than advantageous.
3.
# Add NA as a factor value
housing$Alley <- addNA(housing$Alley)
housing$BsmtQual <- addNA(housing$BsmtQual)
housing$BsmtCond <- addNA(housing$BsmtCond)
housing$BsmtExposure <- addNA(housing$BsmtExposure)
housing$BsmtFinType1 <- addNA(housing$BsmtFinType1)
housing$BsmtFinType2 <- addNA(housing$BsmtFinType2)
housing$FireplaceQu <- addNA(housing$FireplaceQu)
housing$GarageType <- addNA(housing$GarageType)
housing$GarageFinish <- addNA(housing$GarageFinish)
housing$GarageQual <- addNA(housing$GarageQual)
housing$GarageCond <- addNA(housing$GarageCond)
housing$PoolQC <- addNA(housing$PoolQC)
housing$Fence <- addNA(housing$Fence)
housing$MiscFeature <- addNA(housing$MiscFeature)
# Change relevant NA numerical values to 0
housing["GarageYrBlt"][is.na(housing["GarageYrBlt"])] <- 04.
# Check NA's
sapply(housing, function(x) sum(is.na(x)))## MSSubClass MSZoning LotFrontage LotArea Street
## 0 0 259 0 0
## Alley LotShape LandContour Utilities LotConfig
## 0 0 0 0 0
## LandSlope Neighborhood Condition1 Condition2 BldgType
## 0 0 0 0 0
## HouseStyle OverallQual OverallCond YearBuilt YearRemodAdd
## 0 0 0 0 0
## RoofStyle RoofMatl Exterior1st Exterior2nd MasVnrType
## 0 0 0 0 8
## MasVnrArea ExterQual ExterCond Foundation BsmtQual
## 8 0 0 0 0
## BsmtCond BsmtExposure BsmtFinType1 BsmtFinSF1 BsmtFinType2
## 0 0 0 0 0
## BsmtFinSF2 BsmtUnfSF TotalBsmtSF Heating HeatingQC
## 0 0 0 0 0
## CentralAir Electrical X1stFlrSF X2ndFlrSF LowQualFinSF
## 0 1 0 0 0
## GrLivArea BsmtFullBath BsmtHalfBath FullBath HalfBath
## 0 0 0 0 0
## BedroomAbvGr KitchenAbvGr KitchenQual TotRmsAbvGrd Functional
## 0 0 0 0 0
## Fireplaces FireplaceQu GarageType GarageYrBlt GarageFinish
## 0 0 0 0 0
## GarageCars GarageArea GarageQual GarageCond PavedDrive
## 0 0 0 0 0
## WoodDeckSF OpenPorchSF EnclosedPorch X3SsnPorch ScreenPorch
## 0 0 0 0 0
## PoolArea PoolQC Fence MiscFeature MiscVal
## 0 0 0 0 0
## MoSold YrSold SaleType SaleCondition SalePrice
## 0 0 0 0 0After replacing NA’s I have two variables that still contain NA’s: LotFrontage with 259 (17.7%), Electrical with 1 (.068%), MasVnrType with 8 (.54%) and MasVnrArea with 8 (.54%) of the data.
5.
#drop electrical NA rows & drop MasVnrType NA rows
library(tidyr)
housing <- housing %>% drop_na(Electrical) # 1 row
housing <- housing %>% drop_na(MasVnrType) # 8 rows
# Check row with NA's
housing[rowSums(is.na(housing)) > 0, ]## MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape LandContour
## 8 60 RL NA 10382 Pave <NA> IR1 Lvl
## 13 20 RL NA 12968 Pave <NA> IR2 Lvl
## 15 20 RL NA 10920 Pave <NA> IR1 Lvl
## 17 20 RL NA 11241 Pave <NA> IR1 Lvl
## 25 20 RL NA 8246 Pave <NA> IR1 Lvl
## 32 20 RL NA 8544 Pave <NA> IR1 Lvl
## 43 85 RL NA 9180 Pave <NA> IR1 Lvl
## 44 20 RL NA 9200 Pave <NA> IR1 Lvl
## 51 60 RL NA 13869 Pave <NA> IR2 Lvl
## 65 60 RL NA 9375 Pave <NA> Reg Lvl
## 67 20 RL NA 19900 Pave <NA> Reg Lvl
## 77 20 RL NA 8475 Pave <NA> IR1 Lvl
## 85 80 RL NA 8530 Pave <NA> IR1 Lvl
## 96 60 RL NA 9765 Pave <NA> IR2 Lvl
## 101 20 RL NA 10603 Pave <NA> IR1 Lvl
## 105 50 RM NA 7758 Pave <NA> Reg Lvl
## 112 80 RL NA 7750 Pave <NA> IR1 Lvl
## 114 20 RL NA 21000 Pave <NA> Reg Bnk
## 117 20 RL NA 11616 Pave <NA> Reg Lvl
## 121 80 RL NA 21453 Pave <NA> IR1 Low
## 127 120 RL NA 4928 Pave <NA> IR1 Lvl
## 132 60 RL NA 12224 Pave <NA> IR1 Lvl
## 134 20 RL NA 6853 Pave <NA> IR1 Lvl
## 137 20 RL NA 10355 Pave <NA> IR1 Lvl
## 148 60 RL NA 9505 Pave <NA> IR1 Lvl
## 150 50 RM NA 6240 Pave <NA> Reg Lvl
## 153 60 RL NA 14803 Pave <NA> IR1 Lvl
## 154 20 RL NA 13500 Pave <NA> Reg Lvl
## 161 20 RL NA 11120 Pave <NA> IR1 Lvl
## 167 20 RL NA 10708 Pave <NA> IR1 Lvl
## 170 20 RL NA 16669 Pave <NA> IR1 Lvl
## 171 50 RM NA 12358 Pave <NA> IR1 Lvl
## 178 50 RL NA 13650 Pave <NA> Reg Lvl
## 181 160 FV NA 2117 Pave <NA> Reg Lvl
## 187 80 RL NA 9947 Pave <NA> IR1 Lvl
## 192 60 RL NA 7472 Pave <NA> IR1 Lvl
## 204 120 RM NA 4438 Pave <NA> Reg Lvl
## 208 20 RL NA 12493 Pave <NA> IR1 Lvl
## 209 60 RL NA 14364 Pave <NA> IR1 Low
## 215 60 RL NA 10900 Pave <NA> IR1 Lvl
## 219 50 RL NA 15660 Pave <NA> IR1 Lvl
## 222 60 RL NA 8068 Pave <NA> IR1 Lvl
## 237 60 RL NA 9453 Pave <NA> IR1 Lvl
## 244 60 RL NA 8880 Pave <NA> IR1 Lvl
## 249 50 RL NA 159000 Pave <NA> IR2 Low
## 269 20 RL NA 7917 Pave <NA> IR1 Lvl
## 287 20 RL NA 8125 Pave <NA> IR1 Lvl
## 288 20 RL NA 9819 Pave <NA> IR1 Lvl
## 293 60 RL NA 16659 Pave <NA> IR1 Lvl
## 307 50 RM NA 7920 Pave Grvl IR1 Lvl
## 308 30 RL NA 12342 Pave <NA> IR1 Lvl
## 310 60 RL NA 7685 Pave <NA> IR1 Lvl
## 319 80 RL NA 14115 Pave <NA> Reg Lvl
## 328 75 RL NA 11888 Pave Pave IR1 Bnk
## 330 90 RL NA 10624 Pave <NA> IR1 Lvl
## 335 190 RL NA 164660 Grvl <NA> IR1 HLS
## 342 90 RL NA 8544 Pave <NA> Reg Lvl
## 346 20 RL NA 12772 Pave <NA> IR1 Lvl
## 347 20 RL NA 17600 Pave <NA> IR1 Lvl
## 351 120 RL NA 5271 Pave <NA> IR1 Low
## 356 20 RL NA 9248 Pave <NA> IR1 Lvl
## 360 85 RL NA 7540 Pave <NA> IR1 Lvl
## 361 50 RL NA 9144 Pave Pave Reg Lvl
## 364 60 RL NA 18800 Pave <NA> IR1 Lvl
## 366 20 RL NA 9500 Pave <NA> IR1 Lvl
## 369 20 RL NA 9830 Pave <NA> IR1 Lvl
## 370 60 RL NA 8121 Pave <NA> IR1 Lvl
## 375 30 RL NA 10020 Pave <NA> IR1 Low
## 384 60 RL NA 53107 Pave <NA> IR2 Low
## 392 20 RL NA 8339 Pave <NA> IR1 Lvl
## 393 30 RL NA 7446 Pave <NA> Reg Lvl
## 404 60 RL NA 10364 Pave <NA> IR1 Lvl
## 405 20 RL NA 9991 Pave <NA> IR1 Lvl
## 412 20 FV NA 4403 Pave <NA> IR2 Lvl
## 421 20 RL NA 16635 Pave <NA> IR1 Lvl
## 426 80 RL NA 12800 Pave <NA> Reg Low
## 447 60 RL NA 11214 Pave <NA> IR1 Lvl
## 452 60 RL NA 9303 Pave <NA> IR1 Lvl
## 457 20 RL NA 53227 Pave <NA> IR1 Low
## 458 70 RM NA 5100 Pave Grvl Reg Lvl
## 459 50 RL NA 7015 Pave <NA> IR1 Bnk
## 465 120 RM NA 3072 Pave <NA> Reg Lvl
## 470 120 RL NA 6820 Pave <NA> IR1 Lvl
## 484 20 RL NA 7758 Pave <NA> IR1 Lvl
## 490 160 RM NA 2665 Pave <NA> Reg Lvl
## 496 20 RL NA 12692 Pave <NA> IR1 Lvl
## 516 80 RL NA 10448 Pave <NA> IR1 Lvl
## 518 60 RL NA 9531 Pave <NA> IR1 Lvl
## 536 20 RL NA 12735 Pave <NA> IR1 Lvl
## 537 20 RL NA 11553 Pave <NA> IR1 Lvl
## 538 20 RL NA 11423 Pave <NA> Reg Lvl
## 540 60 RL NA 11000 Pave <NA> Reg Lvl
## 544 50 RL NA 13837 Pave <NA> IR1 Lvl
## 558 120 RL NA 3196 Pave <NA> Reg Lvl
## 559 20 RL NA 11341 Pave <NA> IR1 Lvl
## 563 60 RL NA 13346 Pave <NA> IR1 Lvl
## 568 90 RL NA 7032 Pave <NA> IR1 Lvl
## 579 20 RL NA 14585 Pave <NA> IR1 Lvl
## 592 120 RM NA 4435 Pave <NA> Reg Lvl
## 609 60 RL NA 11050 Pave <NA> Reg Lvl
## 610 80 RL NA 10395 Pave <NA> IR1 Lvl
## 611 60 RL NA 11885 Pave <NA> Reg Lvl
## 615 60 RL NA 7861 Pave <NA> IR1 Lvl
## 622 160 FV NA 2117 Pave <NA> Reg Lvl
## 625 20 RL NA 12342 Pave <NA> IR1 Lvl
## 640 60 FV NA 7050 Pave <NA> Reg Lvl
## 644 20 RL NA 10530 Pave <NA> IR1 Lvl
## 658 60 RL NA 12384 Pave <NA> Reg Lvl
## 664 60 RL NA 18450 Pave <NA> IR1 Lvl
## 666 20 RL NA 14175 Pave <NA> Reg Bnk
## 670 20 RL NA 11250 Pave <NA> IR1 Lvl
## 677 20 RL NA 9945 Pave <NA> IR1 Lvl
## 680 120 RL NA 2887 Pave <NA> Reg HLS
## 683 160 RL NA 5062 Pave <NA> IR1 Lvl
## 685 160 FV NA 5105 Pave <NA> IR2 Lvl
## 688 120 RM NA 4426 Pave <NA> Reg Lvl
## 704 20 RL NA 115149 Pave <NA> IR2 Low
## 707 20 RL NA 7162 Pave <NA> IR1 Lvl
## 712 60 RL NA 13517 Pave <NA> IR1 Lvl
## 718 120 RL NA 6563 Pave <NA> IR1 Low
## 719 120 RM NA 4426 Pave <NA> Reg Lvl
## 724 20 RL NA 21695 Pave <NA> IR1 Lvl
## 732 20 RL NA 8978 Pave <NA> IR1 Lvl
## 743 60 RL NA 8963 Pave <NA> IR1 Lvl
## 744 60 RL NA 8795 Pave <NA> IR1 Lvl
## 749 60 RL NA 7750 Pave <NA> Reg Lvl
## 755 60 RL NA 11616 Pave <NA> IR1 Lvl
## 768 85 RL NA 7252 Pave <NA> IR1 Lvl
## 781 85 RL NA 9101 Pave <NA> IR1 Lvl
## 783 20 RL NA 9790 Pave <NA> Reg Lvl
## 787 60 RL NA 12205 Pave <NA> IR1 Low
## 789 80 RL NA 11333 Pave <NA> IR1 Lvl
## 792 60 RL NA 10832 Pave <NA> IR1 Lvl
## 809 120 RM NA 4438 Pave <NA> Reg Lvl
## 814 20 RL NA 11425 Pave <NA> IR1 Lvl
## 815 20 RL NA 13265 Pave <NA> IR1 Lvl
## 820 60 RL NA 12394 Pave <NA> IR1 Lvl
## 826 60 RL NA 28698 Pave <NA> IR2 Low
## 838 70 RH NA 12155 Pave <NA> IR1 Lvl
## 843 85 RL NA 16647 Pave <NA> IR1 Lvl
## 849 120 RL NA 3196 Pave <NA> Reg Lvl
## 851 80 RL NA 12095 Pave <NA> IR1 Lvl
## 853 20 RL NA 6897 Pave <NA> IR1 Lvl
## 854 80 RL NA 10970 Pave <NA> IR1 Low
## 857 60 RL NA 11029 Pave <NA> IR1 Lvl
## 863 20 RL NA 8750 Pave <NA> IR1 Lvl
## 866 60 RL NA 14762 Pave <NA> IR2 Lvl
## 877 20 RL NA 7000 Pave <NA> IR1 Lvl
## 880 60 RL NA 9636 Pave <NA> IR1 Lvl
## 891 20 RL NA 13284 Pave <NA> Reg Lvl
## 898 20 RL NA 7340 Pave <NA> IR1 Lvl
## 902 20 RL NA 6173 Pave <NA> IR1 Lvl
## 906 20 RL NA 8885 Pave <NA> IR1 Low
## 909 20 RL NA 9286 Pave <NA> IR1 Lvl
## 915 20 RL NA 17140 Pave <NA> Reg Lvl
## 923 20 RL NA 15611 Pave <NA> IR1 Lvl
## 925 60 RL NA 9900 Pave <NA> Reg Lvl
## 926 20 RL NA 11838 Pave <NA> Reg Lvl
## 927 60 RL NA 13006 Pave <NA> IR1 Lvl
## 936 70 RL NA 24090 Pave <NA> Reg Lvl
## 938 60 RL NA 8755 Pave <NA> IR1 Lvl
## 941 20 RL NA 14375 Pave <NA> IR1 Lvl
## 950 60 RL NA 11075 Pave <NA> IR1 Lvl
## 958 60 RL NA 12227 Pave <NA> IR1 Lvl
## 964 20 RL NA 7390 Pave <NA> IR1 Lvl
## 971 160 FV NA 2651 Pave <NA> Reg Lvl
## 975 85 RL NA 12122 Pave <NA> IR1 Lvl
## 978 60 RL NA 11250 Pave <NA> Reg Lvl
## 983 60 RL NA 12046 Pave <NA> IR1 Lvl
## 991 20 RL NA 10659 Pave <NA> IR1 Lvl
## 992 20 RL NA 11717 Pave <NA> IR1 Lvl
## 998 90 RL NA 11500 Pave <NA> IR1 Lvl
## 1001 20 RL NA 12155 Pave <NA> IR3 Lvl
## 1012 120 RL NA 5814 Pave <NA> IR1 Lvl
## 1013 80 RL NA 10784 Pave <NA> IR1 Lvl
## 1019 20 RL NA 15498 Pave <NA> IR1 Lvl
## 1025 190 RH NA 7082 Pave <NA> Reg Lvl
## 1027 60 RL NA 14541 Pave <NA> IR1 Lvl
## 1028 20 RL NA 8125 Pave <NA> Reg Lvl
## 1030 20 RL NA 11500 Pave <NA> IR1 Lvl
## 1032 60 RL NA 9240 Pave <NA> Reg Lvl
## 1036 60 RL NA 9130 Pave <NA> Reg Lvl
## 1040 20 RL NA 13680 Pave <NA> IR1 Lvl
## 1052 60 RL NA 29959 Pave <NA> IR2 Lvl
## 1054 50 RL NA 11275 Pave <NA> IR1 HLS
## 1059 20 RL NA 11000 Pave <NA> IR1 Lvl
## 1072 20 RL NA 15870 Pave <NA> IR1 Lvl
## 1079 60 RL NA 13031 Pave <NA> IR2 Lvl
## 1081 160 RM NA 1974 Pave <NA> Reg Lvl
## 1092 120 RL NA 3696 Pave <NA> Reg Lvl
## 1103 60 RL NA 8063 Pave <NA> Reg Lvl
## 1105 60 RL NA 8000 Pave <NA> Reg Lvl
## 1111 80 RL NA 7750 Pave <NA> Reg Lvl
## 1117 20 RL NA 8926 Pave <NA> IR1 Lvl
## 1119 80 RL NA 9125 Pave <NA> IR1 Lvl
## 1133 20 RL NA 9819 Pave <NA> IR1 Lvl
## 1136 60 RL NA 10304 Pave <NA> IR1 Lvl
## 1138 20 RL NA 9000 Pave <NA> Reg Lvl
## 1141 20 RL NA 11200 Pave <NA> Reg Lvl
## 1143 50 RM NA 5700 Pave <NA> Reg Lvl
## 1148 30 RM NA 5890 Pave <NA> Reg Lvl
## 1149 60 RL NA 13700 Pave <NA> IR1 Lvl
## 1156 20 RL NA 14778 Pave <NA> IR1 Low
## 1159 80 RL NA 16157 Pave <NA> IR1 Lvl
## 1172 50 RM NA 3950 Pave Grvl Reg Bnk
## 1175 60 RL NA 11170 Pave <NA> IR2 Lvl
## 1185 190 RL NA 32463 Pave <NA> Reg Low
## 1188 120 RM NA 4500 Pave <NA> Reg Lvl
## 1201 20 RH NA 8900 Pave <NA> Reg Lvl
## 1208 80 RL NA 10246 Pave <NA> IR1 Lvl
## 1225 90 RL NA 18890 Pave <NA> IR1 Lvl
## 1228 20 RL NA 12160 Pave <NA> IR1 Lvl
## 1238 70 RL NA 11435 Pave <NA> IR1 HLS
## 1241 80 RL NA 12328 Pave <NA> IR1 Lvl
## 1245 120 RL NA 3136 Pave <NA> IR1 Lvl
## 1247 60 RL NA 17542 Pave <NA> IR1 Lvl
## 1254 60 RL NA 24682 Pave <NA> IR3 Lvl
## 1256 50 RL NA 11250 Pave <NA> Reg Lvl
## 1262 50 RL NA 14100 Pave <NA> IR1 Lvl
## 1264 40 RL NA 23595 Pave <NA> Reg Low
## 1265 20 RL NA 9156 Pave <NA> IR1 Lvl
## 1266 20 RL NA 13526 Pave <NA> IR1 Lvl
## 1270 60 RL NA 12936 Pave <NA> IR1 Lvl
## 1271 80 RL NA 17871 Pave <NA> IR1 Lvl
## 1279 20 RL NA 9790 Pave <NA> Reg Lvl
## 1280 20 RL NA 36500 Pave <NA> IR1 Low
## 1283 80 RL NA 14112 Pave <NA> IR1 Lvl
## 1293 60 RL NA 10762 Pave <NA> IR1 Lvl
## 1294 70 RL NA 7500 Pave <NA> IR1 Bnk
## 1302 20 RL NA 7153 Pave <NA> Reg Lvl
## 1305 60 RL NA 9572 Pave <NA> IR1 Lvl
## 1311 20 RL NA 14781 Pave <NA> IR2 Lvl
## 1314 20 RL NA 6627 Pave <NA> IR1 Lvl
## 1335 60 RL NA 9375 Pave <NA> Reg Lvl
## 1339 20 RL NA 20781 Pave <NA> IR2 Lvl
## 1341 20 RL NA 16196 Pave <NA> IR3 Low
## 1347 60 RL NA 10316 Pave <NA> IR1 Lvl
## 1349 20 RL NA 9477 Pave <NA> Reg Lvl
## 1350 20 RL NA 12537 Pave <NA> IR1 Lvl
## 1351 160 FV NA 2117 Pave <NA> Reg Lvl
## 1355 50 RL NA 12513 Pave <NA> IR1 Lvl
## 1358 60 FV NA 7500 Pave <NA> Reg Lvl
## 1361 120 RM NA 4435 Pave <NA> Reg Lvl
## 1366 20 RL NA 11400 Pave <NA> Reg Lvl
## 1373 20 RL NA 12925 Pave <NA> IR1 Lvl
## 1375 30 RL NA 25339 Pave <NA> Reg Lvl
## 1388 20 RL NA 57200 Pave <NA> IR1 Bnk
## 1399 20 RL NA 8780 Pave <NA> IR1 Lvl
## 1409 60 RL NA 16545 Pave <NA> IR1 Lvl
## 1411 20 RL NA 16381 Pave <NA> IR1 Lvl
## 1415 80 RL NA 19690 Pave <NA> IR1 Lvl
## 1416 20 RL NA 9503 Pave <NA> Reg Lvl
## 1421 20 RL NA 12546 Pave <NA> IR1 Lvl
## 1423 120 RL NA 4928 Pave <NA> IR1 Lvl
## 1433 120 RM NA 4426 Pave <NA> Reg Lvl
## 1435 30 RL NA 8854 Pave <NA> Reg Lvl
## 1438 20 RL NA 26142 Pave <NA> IR1 Lvl
## Utilities LotConfig LandSlope Neighborhood Condition1 Condition2 BldgType
## 8 AllPub Corner Gtl NWAmes PosN Norm 1Fam
## 13 AllPub Inside Gtl Sawyer Norm Norm 1Fam
## 15 AllPub Corner Gtl NAmes Norm Norm 1Fam
## 17 AllPub CulDSac Gtl NAmes Norm Norm 1Fam
## 25 AllPub Inside Gtl Sawyer Norm Norm 1Fam
## 32 AllPub CulDSac Gtl Sawyer Norm Norm 1Fam
## 43 AllPub CulDSac Gtl SawyerW Norm Norm 1Fam
## 44 AllPub CulDSac Gtl CollgCr Norm Norm 1Fam
## 51 AllPub Corner Gtl Gilbert Norm Norm 1Fam
## 65 AllPub Inside Gtl CollgCr Norm Norm 1Fam
## 67 AllPub Inside Gtl NAmes PosA Norm 1Fam
## 77 AllPub Inside Gtl NAmes Norm Norm 1Fam
## 85 AllPub Inside Gtl Gilbert Norm Norm 1Fam
## 96 AllPub Corner Gtl Gilbert Norm Norm 1Fam
## 101 AllPub Inside Gtl NWAmes Norm Norm 1Fam
## 105 AllPub Corner Gtl IDOTRR Norm Norm 1Fam
## 112 AllPub Inside Gtl Gilbert Norm Norm 1Fam
## 114 AllPub Corner Gtl Crawfor Norm Norm 1Fam
## 117 AllPub Inside Gtl Sawyer Norm Norm 1Fam
## 121 AllPub CulDSac Sev ClearCr Norm Norm 1Fam
## 127 AllPub Inside Gtl NPkVill Norm Norm TwnhsE
## 132 AllPub Corner Gtl Gilbert Norm Norm 1Fam
## 134 AllPub Inside Gtl Timber Norm Norm 1Fam
## 137 AllPub Corner Gtl NAmes Norm Norm 1Fam
## 148 AllPub CulDSac Gtl Gilbert Norm Norm 1Fam
## 150 AllPub Inside Gtl BrkSide Norm Norm 1Fam
## 153 AllPub CulDSac Gtl NWAmes Norm Norm 1Fam
## 154 AllPub Inside Gtl ClearCr Norm Norm 1Fam
## 161 AllPub CulDSac Gtl Veenker Norm Norm 1Fam
## 167 AllPub Inside Gtl ClearCr Norm Norm 1Fam
## 170 AllPub Corner Gtl Timber Norm Norm 1Fam
## 171 AllPub Inside Gtl OldTown Feedr Norm 1Fam
## 178 AllPub Inside Gtl Sawyer Norm Norm 1Fam
## 181 AllPub Inside Gtl Somerst Norm Norm Twnhs
## 187 AllPub CulDSac Gtl Mitchel Norm Norm 1Fam
## 192 AllPub CulDSac Gtl NAmes Norm Norm 1Fam
## 204 AllPub Inside Gtl CollgCr Norm Norm TwnhsE
## 208 AllPub Inside Gtl NAmes Norm Norm 1Fam
## 209 AllPub Inside Mod SawyerW Norm Norm 1Fam
## 215 AllPub FR2 Gtl CollgCr Norm Norm 1Fam
## 219 AllPub Corner Gtl Crawfor Norm Norm 1Fam
## 222 AllPub Inside Gtl Gilbert Norm Norm 1Fam
## 237 AllPub CulDSac Gtl SawyerW RRNe Norm 1Fam
## 244 AllPub Inside Gtl SawyerW Norm Norm 1Fam
## 249 AllPub CulDSac Sev ClearCr Norm Norm 1Fam
## 269 AllPub Corner Gtl Edwards Norm Norm 1Fam
## 287 AllPub Corner Gtl NAmes Norm Norm 1Fam
## 288 AllPub Inside Gtl Sawyer Norm Norm 1Fam
## 293 AllPub Corner Gtl NWAmes PosA Norm 1Fam
## 307 AllPub Inside Gtl IDOTRR Artery Norm 1Fam
## 308 AllPub Inside Gtl Edwards Norm Norm 1Fam
## 310 AllPub Inside Gtl Gilbert Norm Norm 1Fam
## 319 AllPub Inside Gtl NWAmes Norm Norm 1Fam
## 328 AllPub Inside Gtl BrkSide PosN Norm 1Fam
## 330 AllPub Inside Gtl NAmes Norm Norm Duplex
## 335 AllPub Corner Sev Timber Norm Norm 2fmCon
## 342 AllPub Inside Gtl NAmes Norm Norm Duplex
## 346 AllPub CulDSac Gtl NAmes Norm Norm 1Fam
## 347 AllPub Inside Gtl NAmes Norm Norm 1Fam
## 351 AllPub Inside Mod ClearCr Norm Norm 1Fam
## 356 AllPub Inside Gtl Gilbert Norm Norm 1Fam
## 360 AllPub CulDSac Gtl Mitchel Norm Norm 1Fam
## 361 AllPub Inside Gtl BrkSide Norm Norm 1Fam
## 364 AllPub FR2 Gtl NWAmes Norm Norm 1Fam
## 366 AllPub Inside Gtl NAmes Norm Norm 1Fam
## 369 AllPub Corner Gtl NAmes Norm Norm 1Fam
## 370 AllPub Inside Gtl Gilbert Norm Norm 1Fam
## 375 AllPub Inside Sev Edwards Norm Norm 1Fam
## 384 AllPub Corner Mod ClearCr Feedr Norm 1Fam
## 392 AllPub Inside Gtl NAmes Norm Norm 1Fam
## 393 AllPub Corner Gtl BrkSide Feedr Norm 1Fam
## 404 AllPub Inside Gtl Gilbert Norm Norm 1Fam
## 405 AllPub Corner Gtl Sawyer Feedr Norm 1Fam
## 412 AllPub Inside Gtl Somerst Norm Norm 1Fam
## 421 AllPub FR2 Gtl NWAmes Norm Norm 1Fam
## 426 AllPub Inside Mod SawyerW Norm Norm 1Fam
## 447 AllPub Corner Gtl Gilbert Norm Norm 1Fam
## 452 AllPub Corner Gtl Timber Norm Norm 1Fam
## 457 AllPub CulDSac Mod ClearCr Norm Norm 1Fam
## 458 AllPub Inside Gtl OldTown Norm Norm 1Fam
## 459 AllPub Corner Gtl BrkSide Norm Norm 1Fam
## 465 AllPub Inside Gtl Blmngtn Norm Norm TwnhsE
## 470 AllPub Corner Gtl StoneBr Norm Norm TwnhsE
## 484 AllPub Corner Gtl Sawyer Norm Norm 1Fam
## 490 AllPub Inside Gtl MeadowV Norm Norm TwnhsE
## 496 AllPub Inside Gtl NoRidge Norm Norm 1Fam
## 516 AllPub Corner Gtl NWAmes Norm Norm 1Fam
## 518 AllPub CulDSac Gtl CollgCr Norm Norm 1Fam
## 536 AllPub FR2 Gtl NAmes Norm Norm 1Fam
## 537 AllPub Inside Gtl Sawyer Norm Norm 1Fam
## 538 AllPub Inside Gtl CollgCr Norm Norm 1Fam
## 540 AllPub FR2 Gtl NoRidge Norm Norm 1Fam
## 544 AllPub Corner Gtl NWAmes Norm Norm 1Fam
## 558 AllPub Inside Gtl Blmngtn Norm Norm TwnhsE
## 559 AllPub Inside Gtl Sawyer Norm Norm 1Fam
## 563 AllPub CulDSac Gtl NoRidge Norm Norm 1Fam
## 568 AllPub Corner Gtl NAmes Norm Norm Duplex
## 579 AllPub CulDSac Gtl NAmes Norm Norm 1Fam
## 592 AllPub Inside Gtl CollgCr Norm Norm TwnhsE
## 609 AllPub Inside Gtl CollgCr PosN Norm 1Fam
## 610 AllPub FR2 Gtl NWAmes Norm Norm 1Fam
## 611 AllPub Inside Gtl CollgCr Norm Norm 1Fam
## 615 AllPub Inside Gtl Gilbert Norm Norm 1Fam
## 622 AllPub Inside Gtl Somerst Norm Norm TwnhsE
## 625 AllPub Inside Gtl NAmes Norm Norm 1Fam
## 640 AllPub Inside Gtl Somerst Norm Norm 1Fam
## 644 AllPub Corner Gtl NAmes Norm Norm 1Fam
## 658 AllPub CulDSac Gtl NWAmes Norm Norm 1Fam
## 664 AllPub Inside Gtl NAmes Norm Norm 1Fam
## 666 AllPub Corner Mod Sawyer Norm Norm 1Fam
## 670 AllPub Inside Gtl Veenker Norm Norm 1Fam
## 677 AllPub Inside Gtl Sawyer Norm Norm 1Fam
## 680 AllPub Inside Gtl ClearCr Norm Norm 1Fam
## 683 AllPub CulDSac Gtl StoneBr Norm Norm TwnhsE
## 685 AllPub FR2 Gtl Somerst Norm Norm TwnhsE
## 688 AllPub Inside Gtl CollgCr Norm Norm TwnhsE
## 704 AllPub CulDSac Sev ClearCr Norm Norm 1Fam
## 707 AllPub Inside Gtl Sawyer Norm Norm 1Fam
## 712 AllPub CulDSac Gtl Sawyer RRAe Norm 1Fam
## 718 AllPub CulDSac Mod StoneBr Norm Norm 1Fam
## 719 AllPub Inside Gtl CollgCr Norm Norm TwnhsE
## 724 AllPub Corner Gtl Crawfor Norm Norm 1Fam
## 732 AllPub Corner Gtl Sawyer Norm Norm 1Fam
## 743 AllPub Inside Gtl NWAmes Norm Norm 1Fam
## 744 AllPub Inside Gtl Gilbert Norm Norm 1Fam
## 749 AllPub Inside Gtl Gilbert RRAn Norm 1Fam
## 755 AllPub CulDSac Gtl Sawyer Norm Norm 1Fam
## 768 AllPub CulDSac Gtl Sawyer Norm Norm 1Fam
## 781 AllPub Corner Gtl Mitchel Norm Norm 1Fam
## 783 AllPub Inside Gtl NWAmes Feedr Norm 1Fam
## 787 AllPub Inside Gtl ClearCr Norm Norm 1Fam
## 789 AllPub Corner Gtl Mitchel Norm Norm 1Fam
## 792 AllPub Corner Gtl Gilbert Norm Norm 1Fam
## 809 AllPub Inside Gtl CollgCr Norm Norm TwnhsE
## 814 AllPub Corner Gtl NAmes Norm Norm 1Fam
## 815 AllPub CulDSac Gtl Mitchel Norm Norm 1Fam
## 820 AllPub Corner Gtl Gilbert Norm Norm 1Fam
## 826 AllPub CulDSac Sev ClearCr Norm Norm 1Fam
## 838 AllPub Inside Gtl SWISU Norm Norm 1Fam
## 843 AllPub CulDSac Gtl Sawyer RRAe Norm 1Fam
## 849 AllPub Inside Gtl Blmngtn Norm Norm TwnhsE
## 851 AllPub Corner Gtl NAmes Norm Norm 1Fam
## 853 AllPub Corner Gtl Sawyer Norm Norm 1Fam
## 854 AllPub Inside Mod CollgCr Norm Norm 1Fam
## 857 AllPub Corner Gtl NWAmes PosA Norm 1Fam
## 863 AllPub Inside Gtl NAmes Norm Norm 1Fam
## 866 AllPub Corner Gtl Gilbert Feedr Norm 1Fam
## 877 AllPub CulDSac Gtl CollgCr Norm Norm 1Fam
## 880 AllPub Corner Gtl Gilbert Norm Norm 1Fam
## 891 AllPub Inside Gtl Sawyer PosN Norm 1Fam
## 898 AllPub Inside Gtl NAmes Norm Norm 1Fam
## 902 AllPub Inside Gtl Sawyer Norm Norm 1Fam
## 906 AllPub Inside Mod Mitchel Norm Norm 1Fam
## 909 AllPub CulDSac Mod CollgCr Norm Norm 1Fam
## 915 AllPub Inside Gtl Edwards Norm Norm 1Fam
## 923 AllPub Corner Gtl NWAmes Norm Norm 1Fam
## 925 AllPub Inside Gtl NWAmes Feedr Norm 1Fam
## 926 AllPub Inside Gtl CollgCr Norm Norm 1Fam
## 927 AllPub Corner Gtl Gilbert Norm Norm 1Fam
## 936 AllPub Inside Gtl ClearCr Norm Norm 1Fam
## 938 AllPub FR2 Gtl Gilbert RRNn Norm 1Fam
## 941 NoSeWa CulDSac Gtl Timber Norm Norm 1Fam
## 950 AllPub Inside Mod Mitchel Norm Norm 1Fam
## 958 AllPub Corner Gtl NWAmes PosN Norm 1Fam
## 964 AllPub Inside Gtl NAmes Norm Norm 1Fam
## 971 AllPub FR2 Gtl Somerst Norm Norm Twnhs
## 975 AllPub Corner Gtl NAmes Norm Norm 1Fam
## 978 AllPub Corner Gtl CollgCr Norm Norm 1Fam
## 983 AllPub Inside Gtl NWAmes Norm Norm 1Fam
## 991 AllPub Inside Gtl NAmes Norm Norm 1Fam
## 992 AllPub Inside Gtl NWAmes PosA Norm 1Fam
## 998 AllPub Corner Gtl NWAmes Feedr RRAn Duplex
## 1001 AllPub Inside Gtl NAmes PosN Norm 1Fam
## 1012 AllPub CulDSac Gtl StoneBr Norm Norm TwnhsE
## 1013 AllPub FR2 Gtl Gilbert Norm Norm 1Fam
## 1019 AllPub Corner Gtl Timber Norm Norm 1Fam
## 1025 AllPub Inside Gtl SWISU Norm Norm 2fmCon
## 1027 AllPub Corner Gtl NoRidge Norm Norm 1Fam
## 1028 AllPub Inside Gtl CollgCr Norm Norm 1Fam
## 1030 AllPub CulDSac Gtl Edwards Norm Norm 1Fam
## 1032 AllPub Inside Gtl CollgCr Norm Norm 1Fam
## 1036 AllPub Inside Gtl NWAmes Feedr Norm 1Fam
## 1040 AllPub CulDSac Gtl Edwards Norm Norm 1Fam
## 1052 AllPub FR2 Gtl NoRidge Norm Norm 1Fam
## 1054 AllPub Corner Mod Crawfor Norm Norm 1Fam
## 1059 AllPub CulDSac Gtl NAmes Norm Norm 1Fam
## 1072 AllPub Corner Gtl NAmes Norm Norm 1Fam
## 1079 AllPub Corner Gtl Gilbert Norm Norm 1Fam
## 1081 AllPub Inside Gtl MeadowV Norm Norm TwnhsE
## 1092 AllPub Inside Gtl StoneBr Norm Norm TwnhsE
## 1103 AllPub Inside Gtl Gilbert Norm Norm 1Fam
## 1105 AllPub Inside Gtl Gilbert Norm Norm 1Fam
## 1111 AllPub Inside Gtl Gilbert Norm Norm 1Fam
## 1117 AllPub Corner Gtl Edwards Norm Norm 1Fam
## 1119 AllPub Inside Gtl Gilbert Norm Norm 1Fam
## 1133 AllPub Inside Mod Mitchel Norm Norm 1Fam
## 1136 AllPub CulDSac Gtl NWAmes PosN Norm 1Fam
## 1138 AllPub Inside Gtl Sawyer Norm Norm 1Fam
## 1141 AllPub Inside Gtl SawyerW Norm Norm 1Fam
## 1143 AllPub Inside Gtl OldTown Norm Norm 1Fam
## 1148 AllPub Corner Gtl IDOTRR Norm Norm 1Fam
## 1149 AllPub Inside Gtl NAmes Norm Norm 1Fam
## 1156 AllPub CulDSac Gtl Crawfor PosN Norm 1Fam
## 1159 AllPub FR2 Gtl Veenker Feedr Norm 1Fam
## 1172 AllPub Inside Gtl OldTown Artery Norm 1Fam
## 1175 AllPub Corner Gtl Timber Norm Norm 1Fam
## 1185 AllPub Inside Mod Mitchel Norm Norm 2fmCon
## 1188 AllPub FR2 Gtl Mitchel Norm Norm TwnhsE
## 1201 AllPub Inside Gtl SawyerW Norm Norm 1Fam
## 1208 AllPub CulDSac Gtl Sawyer Norm Norm 1Fam
## 1225 AllPub Inside Gtl Sawyer Feedr RRAe Duplex
## 1228 AllPub Inside Gtl NAmes Norm Norm 1Fam
## 1238 AllPub Corner Mod Crawfor Norm Norm 1Fam
## 1241 AllPub Inside Gtl Mitchel Norm Norm 1Fam
## 1245 AllPub Corner Gtl NridgHt Norm Norm TwnhsE
## 1247 AllPub Inside Gtl Veenker Norm Norm 1Fam
## 1254 AllPub CulDSac Gtl Gilbert RRAn Norm 1Fam
## 1256 AllPub Inside Gtl ClearCr Norm Norm 1Fam
## 1262 AllPub Inside Mod Crawfor Norm Norm 1Fam
## 1264 AllPub Inside Sev ClearCr Norm Norm 1Fam
## 1265 AllPub Inside Gtl NWAmes PosN Norm 1Fam
## 1266 AllPub CulDSac Gtl Sawyer Norm Norm 1Fam
## 1270 AllPub CulDSac Gtl NWAmes Norm Norm 1Fam
## 1271 AllPub CulDSac Gtl NWAmes Norm Norm 1Fam
## 1279 AllPub Inside Gtl NWAmes Feedr Norm 1Fam
## 1280 AllPub Inside Mod ClearCr Norm Norm 1Fam
## 1283 AllPub Corner Gtl NAmes Norm Norm 1Fam
## 1293 AllPub CulDSac Gtl Gilbert Norm Norm 1Fam
## 1294 AllPub Inside Gtl Crawfor Norm Norm 1Fam
## 1302 AllPub Inside Gtl SawyerW Norm Norm 1Fam
## 1305 AllPub Inside Gtl NoRidge Norm Norm 1Fam
## 1311 AllPub CulDSac Gtl CollgCr Norm Norm 1Fam
## 1314 AllPub Corner Gtl BrkSide Feedr Norm 1Fam
## 1335 AllPub Inside Gtl CollgCr Norm Norm 1Fam
## 1339 AllPub CulDSac Gtl NWAmes PosN Norm 1Fam
## 1341 AllPub Inside Gtl SawyerW Norm Norm 1Fam
## 1347 AllPub Inside Gtl CollgCr Norm Norm 1Fam
## 1349 AllPub Corner Gtl NAmes Norm Norm 1Fam
## 1350 AllPub CulDSac Gtl NAmes Norm Norm 1Fam
## 1351 AllPub Inside Gtl Somerst Norm Norm Twnhs
## 1355 AllPub FR2 Gtl NAmes Feedr Norm 1Fam
## 1358 AllPub Inside Gtl Somerst Norm Norm 1Fam
## 1361 AllPub Inside Gtl CollgCr Norm Norm TwnhsE
## 1366 AllPub Inside Gtl NoRidge Norm Norm 1Fam
## 1373 AllPub Corner Gtl NAmes Norm Norm 1Fam
## 1375 AllPub Inside Gtl Sawyer Norm Norm 1Fam
## 1388 AllPub Inside Sev Timber Norm Norm 1Fam
## 1399 AllPub Corner Gtl Mitchel Norm Norm 1Fam
## 1409 AllPub Inside Gtl NoRidge Norm Norm 1Fam
## 1411 AllPub Inside Gtl Crawfor Norm Norm 1Fam
## 1415 AllPub CulDSac Gtl Edwards Norm Norm 1Fam
## 1416 AllPub Inside Gtl NAmes Norm Norm 1Fam
## 1421 AllPub Corner Gtl NWAmes Norm Norm 1Fam
## 1423 AllPub Inside Gtl NPkVill Norm Norm TwnhsE
## 1433 AllPub Inside Gtl CollgCr Norm Norm TwnhsE
## 1435 AllPub Inside Gtl BrkSide Norm Norm 1Fam
## 1438 AllPub CulDSac Gtl Mitchel Norm Norm 1Fam
## HouseStyle OverallQual OverallCond YearBuilt YearRemodAdd RoofStyle
## 8 2Story 7 6 1973 1973 Gable
## 13 1Story 5 6 1962 1962 Hip
## 15 1Story 6 5 1960 1960 Hip
## 17 1Story 6 7 1970 1970 Gable
## 25 1Story 5 8 1968 2001 Gable
## 32 1Story 5 6 1966 2006 Gable
## 43 SFoyer 5 7 1983 1983 Gable
## 44 1Story 5 6 1975 1980 Hip
## 51 2Story 6 6 1997 1997 Gable
## 65 2Story 7 5 1997 1998 Gable
## 67 1Story 7 5 1970 1989 Gable
## 77 1Story 4 7 1956 1956 Gable
## 85 SLvl 7 5 1995 1996 Gable
## 96 2Story 6 8 1993 1993 Gable
## 101 1Story 6 7 1977 2001 Gable
## 105 1.5Fin 7 4 1931 1950 Gable
## 112 SLvl 7 5 2000 2000 Gable
## 114 1Story 6 5 1953 1953 Hip
## 117 1Story 5 5 1962 1962 Gable
## 121 SLvl 6 5 1969 1969 Flat
## 127 1Story 6 5 1976 1976 Gable
## 132 2Story 6 5 2000 2000 Gable
## 134 1Story 8 5 2001 2002 Gable
## 137 1Story 5 5 1967 1967 Gable
## 148 2Story 7 5 2001 2001 Gable
## 150 1.5Fin 5 4 1936 1950 Gable
## 153 2Story 6 5 1971 1971 Gable
## 154 1Story 6 7 1960 1975 Flat
## 161 1Story 6 6 1984 1984 Gable
## 167 1Story 5 5 1955 1993 Hip
## 170 1Story 8 6 1981 1981 Hip
## 171 1.5Fin 5 6 1941 1950 Gable
## 178 1.5Fin 5 5 1958 1958 Gable
## 181 2Story 6 5 2000 2000 Gable
## 187 SLvl 7 5 1990 1991 Gable
## 192 2Story 7 9 1972 2004 Gable
## 204 1Story 6 5 2004 2004 Gable
## 208 1Story 4 5 1960 1960 Gable
## 209 2Story 7 5 1988 1989 Gable
## 215 2Story 6 7 1977 1977 Gable
## 219 1.5Fin 7 9 1939 2006 Gable
## 222 2Story 6 5 2002 2002 Gable
## 237 2Story 7 7 1993 2003 Gable
## 244 2Story 7 5 1994 2002 Gable
## 249 1.5Fin 6 7 1958 2006 Gable
## 269 1Story 6 7 1976 1976 Hip
## 287 1Story 4 4 1971 1971 Gable
## 288 1Story 5 5 1967 1967 Gable
## 293 2Story 7 7 1977 1994 Gable
## 307 1.5Fin 6 7 1920 1950 Gable
## 308 1Story 4 5 1940 1950 Gable
## 310 2Story 6 5 1993 1994 Gable
## 319 SLvl 7 5 1980 1980 Gable
## 328 2.5Unf 6 6 1916 1994 Gable
## 330 1Story 5 4 1964 1964 Gable
## 335 1.5Fin 5 6 1965 1965 Gable
## 342 1Story 3 4 1949 1950 Gable
## 346 1Story 6 8 1960 1998 Hip
## 347 1Story 6 5 1960 1960 Gable
## 351 1Story 7 5 1986 1986 Gable
## 356 1Story 6 6 1992 1992 Gable
## 360 SFoyer 6 6 1978 1978 Gable
## 361 1.5Fin 5 5 1940 1982 Gable
## 364 2Story 6 5 1976 1976 Gable
## 366 1Story 6 5 1963 1963 Gable
## 369 1Story 5 7 1959 2006 Gable
## 370 2Story 6 5 2000 2000 Gable
## 375 1Story 1 1 1922 1950 Gable
## 384 2Story 6 5 1992 1992 Gable
## 392 1Story 5 7 1959 1959 Gable
## 393 1Story 4 5 1941 1950 Gable
## 404 2Story 6 5 1995 1996 Gable
## 405 1Story 4 4 1976 1993 Gable
## 412 1Story 7 5 2009 2009 Gable
## 421 1Story 6 7 1977 2000 Gable
## 426 SLvl 7 5 1989 1989 Gable
## 447 2Story 7 5 1998 1999 Gable
## 452 2Story 6 5 1996 1997 Hip
## 457 1Story 4 6 1954 1994 Flat
## 458 2Story 8 7 1925 1996 Hip
## 459 1.5Fin 5 4 1950 1950 Gable
## 465 1Story 7 5 2004 2004 Hip
## 470 1Story 8 5 1985 1985 Gable
## 484 1Story 5 7 1962 2001 Gable
## 490 2Story 5 6 1976 1976 Gable
## 496 1Story 8 5 1992 1993 Hip
## 516 SLvl 6 6 1972 1972 Gable
## 518 2Story 6 5 1998 1998 Gable
## 536 1Story 4 5 1972 1972 Hip
## 537 1Story 5 5 1968 1968 Hip
## 538 1Story 8 5 2001 2002 Gable
## 540 2Story 8 5 2000 2000 Gable
## 544 1.5Fin 7 5 1988 1988 Gable
## 558 1Story 7 5 2003 2004 Gable
## 559 1Story 5 6 1957 1996 Hip
## 563 2Story 7 5 1992 2000 Gable
## 568 SFoyer 5 5 1979 1979 Gable
## 579 1Story 6 6 1960 1987 Gable
## 592 1Story 6 5 2003 2003 Gable
## 609 2Story 9 5 2000 2000 Hip
## 610 SLvl 6 6 1978 1978 Gable
## 611 2Story 8 5 2001 2001 Gable
## 615 2Story 6 5 2002 2003 Gable
## 622 2Story 6 5 2000 2000 Gable
## 625 1Story 5 5 1960 1978 Hip
## 640 2Story 7 5 2001 2001 Gable
## 644 1Story 6 5 1971 1971 Hip
## 658 2Story 7 7 1976 1976 Gable
## 664 2Story 6 5 1965 1979 Flat
## 666 1Story 5 6 1956 1987 Gable
## 670 1Story 6 6 1977 1977 Gable
## 677 1Story 5 5 1961 1961 Hip
## 680 1Story 6 5 1996 1997 Gable
## 683 2Story 7 5 1984 1984 Gable
## 685 2Story 7 5 2004 2004 Gable
## 688 1Story 6 5 2004 2004 Gable
## 704 1Story 7 5 1971 2002 Gable
## 707 1Story 5 7 1966 1966 Gable
## 712 2Story 6 8 1976 2005 Gable
## 718 1Story 8 5 1985 1985 Gable
## 719 1Story 6 5 2004 2004 Gable
## 724 1Story 6 9 1988 2007 Hip
## 732 1Story 5 5 1968 1968 Gable
## 743 2Story 8 9 1976 1996 Hip
## 744 2Story 7 5 2000 2000 Gable
## 749 2Story 7 5 2003 2003 Gable
## 755 2Story 6 5 1978 1978 Hip
## 768 SFoyer 5 5 1982 1982 Hip
## 781 SFoyer 5 6 1978 1978 Gable
## 783 1Story 6 5 1967 1967 Gable
## 787 2Story 6 8 1966 2007 Gable
## 789 SLvl 6 5 1976 1976 Gable
## 792 2Story 7 5 1994 1996 Gable
## 809 1Story 6 5 2004 2004 Gable
## 814 1Story 5 6 1954 1954 Gable
## 815 1Story 8 5 2002 2002 Hip
## 820 2Story 7 5 2003 2003 Gable
## 826 2Story 5 5 1967 1967 Flat
## 838 2Story 6 8 1925 1950 Gable
## 843 SFoyer 5 5 1975 1981 Gable
## 849 1Story 8 5 2003 2003 Gable
## 851 SLvl 6 6 1964 1964 Gable
## 853 1Story 5 8 1962 2010 Gable
## 854 SLvl 6 6 1978 1978 Gable
## 857 2Story 6 7 1968 1984 Gable
## 863 1Story 5 6 1970 1970 Gable
## 866 2Story 5 6 1948 1950 Gable
## 877 1Story 5 8 1978 2005 Gable
## 880 2Story 6 5 1992 1993 Gable
## 891 1Story 5 5 1954 1954 Gable
## 898 1Story 4 6 1971 1971 Gable
## 902 1Story 5 6 1967 1967 Gable
## 906 1Story 5 5 1983 1983 Gable
## 909 1Story 5 7 1977 1989 Gable
## 915 1Story 4 6 1956 1956 Gable
## 923 1Story 5 6 1977 1977 Gable
## 925 2Story 7 5 1968 1968 Gable
## 926 1Story 8 5 2001 2001 Hip
## 927 2Story 7 5 1997 1997 Gable
## 936 2Story 7 7 1940 1950 Gable
## 938 2Story 7 5 1999 1999 Gable
## 941 SLvl 6 6 1958 1958 Gable
## 950 2Story 5 4 1969 1969 Gable
## 958 2Story 6 7 1977 1995 Gable
## 964 1Story 5 7 1955 1955 Hip
## 971 2Story 7 5 2000 2000 Gable
## 975 SFoyer 7 9 1961 2007 Gable
## 978 2Story 8 5 2002 2002 Gable
## 983 2Story 6 6 1976 1976 Gable
## 991 1Story 5 6 1961 1961 Hip
## 992 1Story 6 6 1970 1970 Hip
## 998 1Story 5 6 1976 1976 Gable
## 1001 1Story 6 3 1970 1970 Gable
## 1012 1Story 8 5 1984 1984 Gable
## 1013 SLvl 7 5 1991 1992 Gable
## 1019 1Story 8 6 1976 1976 Hip
## 1025 2Story 5 8 1916 1995 Gable
## 1027 2Story 8 7 1993 1993 Gable
## 1028 1Story 7 5 2002 2002 Gable
## 1030 1Story 4 3 1957 1957 Gable
## 1032 2Story 8 5 2001 2002 Gable
## 1036 2Story 6 8 1966 2000 Hip
## 1040 1Story 3 5 1955 1955 Hip
## 1052 2Story 7 6 1994 1994 Gable
## 1054 1.5Fin 6 7 1932 1950 Gable
## 1059 1Story 5 6 1966 1966 Gable
## 1072 1Story 5 5 1969 1969 Gable
## 1079 2Story 6 5 1995 1996 Gable
## 1081 2Story 4 5 1973 1973 Gable
## 1092 1Story 8 5 1986 1986 Gable
## 1103 2Story 6 5 2000 2000 Gable
## 1105 2Story 6 5 1995 1996 Gable
## 1111 SLvl 8 5 2002 2002 Hip
## 1117 1Story 4 3 1956 1956 Gable
## 1119 SLvl 7 5 1992 1992 Gable
## 1133 1Story 6 5 1977 1977 Gable
## 1136 2Story 5 7 1976 1976 Gable
## 1138 1Story 5 3 1959 1959 Gable
## 1141 1Story 6 5 1985 1985 Gable
## 1143 1.5Fin 7 7 1926 1950 Gable
## 1148 1Story 6 8 1930 2007 Gable
## 1149 2Story 7 6 1965 1988 Gable
## 1156 1Story 6 7 1954 2006 Hip
## 1159 SLvl 5 7 1978 1978 Gable
## 1172 1.5Fin 6 8 1926 2004 Gable
## 1175 2Story 7 5 1990 1991 Gable
## 1185 1Story 4 4 1961 1975 Gable
## 1188 1Story 6 5 1999 1999 Hip
## 1201 1Story 4 4 1966 1966 Gable
## 1208 SLvl 4 9 1965 2001 Gable
## 1225 1.5Fin 5 5 1977 1977 Shed
## 1228 1Story 5 5 1959 1959 Hip
## 1238 2Story 8 7 1929 1950 Gable
## 1241 SLvl 6 5 1976 1976 Gable
## 1245 1Story 7 5 2003 2003 Gable
## 1247 2Story 7 7 1974 2003 Gable
## 1254 2Story 6 5 1999 1999 Gable
## 1256 1.5Fin 4 5 1957 1989 Gable
## 1262 1.5Fin 8 9 1935 1997 Gable
## 1264 1Story 7 6 1979 1979 Shed
## 1265 1Story 6 7 1968 1968 Hip
## 1266 1Story 5 6 1965 1965 Hip
## 1270 2Story 6 6 1972 1972 Gable
## 1271 SLvl 6 5 1967 1976 Gable
## 1279 1Story 6 5 1963 1963 Hip
## 1280 1Story 5 5 1964 1964 Gable
## 1283 SLvl 5 7 1964 1964 Hip
## 1293 2Story 7 5 1999 1999 Gable
## 1294 2Story 6 7 1942 1950 Gable
## 1302 1Story 6 5 1991 1991 Gable
## 1305 2Story 8 5 1990 1990 Gable
## 1311 1Story 8 5 2001 2002 Hip
## 1314 1Story 3 6 1949 1950 Hip
## 1335 2Story 8 5 2002 2002 Gable
## 1339 1Story 7 7 1968 2003 Hip
## 1341 1Story 7 5 1998 1998 Gable
## 1347 2Story 7 5 2000 2000 Gable
## 1349 1Story 5 5 1966 1966 Gable
## 1350 1Story 5 6 1971 2008 Gable
## 1351 2Story 6 5 2000 2000 Gable
## 1355 1.5Fin 4 4 1920 2007 Gable
## 1358 2Story 7 5 2000 2000 Gable
## 1361 1Story 6 5 2003 2004 Gable
## 1366 1Story 10 5 2001 2002 Hip
## 1373 1Story 6 7 1970 1970 Gable
## 1375 1Story 5 7 1918 2007 Gable
## 1388 1Story 5 5 1948 1950 Gable
## 1399 1Story 5 5 1985 1985 Gable
## 1409 2Story 8 5 1998 1998 Gable
## 1411 1Story 6 5 1969 1969 Gable
## 1415 SLvl 6 7 1966 1966 Flat
## 1416 1Story 5 5 1958 1983 Hip
## 1421 1Story 6 7 1981 1981 Gable
## 1423 1Story 6 6 1976 1976 Gable
## 1433 1Story 6 5 2004 2004 Gable
## 1435 1.5Unf 6 6 1916 1950 Gable
## 1438 1Story 5 7 1962 1962 Gable
## RoofMatl Exterior1st Exterior2nd MasVnrType MasVnrArea ExterQual ExterCond
## 8 CompShg HdBoard HdBoard Stone 240 TA TA
## 13 CompShg HdBoard Plywood None 0 TA TA
## 15 CompShg MetalSd MetalSd BrkFace 212 TA TA
## 17 CompShg Wd Sdng Wd Sdng BrkFace 180 TA TA
## 25 CompShg Plywood Plywood None 0 TA Gd
## 32 CompShg HdBoard HdBoard None 0 TA TA
## 43 CompShg HdBoard HdBoard None 0 TA TA
## 44 CompShg VinylSd VinylSd None 0 TA TA
## 51 CompShg VinylSd VinylSd None 0 TA TA
## 65 CompShg VinylSd VinylSd BrkFace 573 TA TA
## 67 CompShg Plywood Plywood BrkFace 287 TA TA
## 77 CompShg VinylSd VinylSd None 0 TA TA
## 85 CompShg HdBoard HdBoard BrkFace 22 TA TA
## 96 CompShg VinylSd VinylSd BrkFace 68 Ex Gd
## 101 CompShg Plywood Plywood BrkFace 28 TA TA
## 105 CompShg Stucco Stucco BrkFace 600 TA Fa
## 112 CompShg VinylSd VinylSd None 0 TA TA
## 114 CompShg Wd Sdng Wd Sdng BrkFace 184 TA Gd
## 117 CompShg Wd Sdng Wd Sdng BrkFace 116 TA TA
## 121 Metal Plywood Plywood None 0 TA TA
## 127 CompShg Plywood Plywood None 0 TA TA
## 132 CompShg VinylSd VinylSd BrkFace 40 Gd TA
## 134 CompShg VinylSd VinylSd BrkFace 136 Gd TA
## 137 CompShg MetalSd MetalSd BrkFace 196 TA TA
## 148 CompShg VinylSd VinylSd BrkFace 180 Gd TA
## 150 CompShg MetalSd MetalSd None 0 TA TA
## 153 CompShg HdBoard HdBoard BrkFace 252 TA TA
## 154 CompShg BrkFace Plywood None 0 TA TA
## 161 CompShg Plywood Plywood None 0 TA TA
## 167 CompShg Wd Sdng Wd Sdng None 0 Gd TA
## 170 WdShake Plywood Plywood BrkFace 653 Gd TA
## 171 CompShg MetalSd MetalSd None 0 TA TA
## 178 CompShg MetalSd MetalSd None 0 Gd Gd
## 181 CompShg MetalSd MetalSd BrkFace 456 Gd TA
## 187 CompShg HdBoard HdBoard None 0 TA TA
## 192 CompShg HdBoard HdBoard BrkFace 138 TA TA
## 204 CompShg VinylSd VinylSd BrkFace 205 Gd TA
## 208 CompShg Wd Sdng Wd Sdng None 0 TA TA
## 209 CompShg Plywood Plywood BrkFace 128 Gd TA
## 215 CompShg HdBoard HdBoard BrkFace 153 TA TA
## 219 CompShg VinylSd VinylSd BrkFace 312 Gd Gd
## 222 CompShg VinylSd VinylSd None 0 Gd TA
## 237 CompShg HdBoard HdBoard None 0 Gd TA
## 244 CompShg VinylSd VinylSd None 0 Gd TA
## 249 CompShg Wd Sdng HdBoard BrkCmn 472 Gd TA
## 269 CompShg HdBoard HdBoard BrkFace 174 TA Gd
## 287 CompShg HdBoard HdBoard None 0 TA TA
## 288 CompShg MetalSd MetalSd BrkFace 31 TA Gd
## 293 CompShg Plywood Plywood BrkFace 34 TA TA
## 307 CompShg MetalSd MetalSd None 0 TA Fa
## 308 CompShg VinylSd VinylSd None 0 TA TA
## 310 CompShg HdBoard HdBoard BrkFace 112 TA TA
## 319 CompShg Plywood Plywood BrkFace 225 TA TA
## 328 CompShg Wd Sdng Wd Shng None 0 TA TA
## 330 CompShg HdBoard HdBoard BrkFace 84 TA TA
## 335 CompShg Plywood Plywood None 0 TA TA
## 342 CompShg Stucco Stucco BrkFace 340 TA TA
## 346 CompShg MetalSd MetalSd None 0 TA Gd
## 347 CompShg Wd Sdng Wd Sdng BrkFace 30 TA TA
## 351 CompShg Wd Sdng Wd Sdng None 0 TA TA
## 356 CompShg HdBoard HdBoard BrkFace 106 TA TA
## 360 CompShg VinylSd VinylSd None 0 TA TA
## 361 CompShg MetalSd MetalSd None 0 TA TA
## 364 CompShg HdBoard HdBoard BrkFace 120 TA TA
## 366 CompShg Plywood Plywood BrkFace 247 TA TA
## 369 CompShg Wd Sdng Wd Sdng None 0 TA Gd
## 370 CompShg VinylSd VinylSd None 0 TA TA
## 375 CompShg Wd Sdng Wd Sdng None 0 Fa Fa
## 384 CompShg HdBoard HdBoard None 0 Gd TA
## 392 CompShg MetalSd MetalSd None 0 TA TA
## 393 CompShg WdShing Wd Shng None 0 TA TA
## 404 CompShg MetalSd MetalSd None 0 TA TA
## 405 CompShg Plywood Plywood None 0 TA TA
## 412 CompShg MetalSd MetalSd Stone 432 Ex TA
## 421 CompShg CemntBd CmentBd Stone 126 Gd TA
## 426 CompShg Wd Sdng Wd Sdng BrkFace 145 Gd TA
## 447 CompShg VinylSd VinylSd None 0 Gd TA
## 452 CompShg VinylSd VinylSd BrkFace 42 Gd TA
## 457 Tar&Grv Plywood Plywood None 0 TA TA
## 458 CompShg Stucco Wd Shng None 0 TA Gd
## 459 CompShg MetalSd MetalSd BrkCmn 161 TA TA
## 465 CompShg VinylSd VinylSd BrkFace 18 Gd TA
## 470 CompShg HdBoard HdBoard None 0 Gd TA
## 484 CompShg HdBoard Plywood None 0 TA Gd
## 490 CompShg CemntBd CmentBd None 0 TA TA
## 496 CompShg BrkFace BrkFace None 0 Gd TA
## 516 CompShg HdBoard HdBoard BrkFace 333 TA TA
## 518 CompShg VinylSd VinylSd None 0 TA TA
## 536 CompShg MetalSd MetalSd None 0 TA TA
## 537 CompShg Plywood Plywood BrkFace 188 TA TA
## 538 CompShg VinylSd VinylSd BrkFace 479 Gd TA
## 540 CompShg VinylSd VinylSd BrkFace 72 Gd TA
## 544 CompShg HdBoard HdBoard BrkFace 178 Gd Gd
## 558 CompShg VinylSd VinylSd BrkFace 18 Gd TA
## 559 CompShg Wd Sdng Wd Sdng BrkFace 180 TA TA
## 563 CompShg HdBoard HdBoard None 0 Gd TA
## 568 CompShg MetalSd MetalSd None 0 TA TA
## 579 CompShg Wd Sdng Wd Sdng BrkFace 85 TA TA
## 592 CompShg VinylSd VinylSd BrkFace 170 Gd TA
## 609 CompShg VinylSd VinylSd BrkFace 204 Gd TA
## 610 CompShg HdBoard HdBoard BrkFace 233 TA TA
## 611 CompShg VinylSd VinylSd BrkFace 108 Gd TA
## 615 CompShg VinylSd VinylSd None 0 Gd TA
## 622 CompShg MetalSd MetalSd BrkFace 513 Gd TA
## 625 CompShg Wd Sdng Wd Sdng None 0 TA TA
## 640 CompShg VinylSd VinylSd None 0 Gd TA
## 644 CompShg Plywood Plywood None 0 TA TA
## 658 CompShg Plywood Plywood BrkFace 233 TA TA
## 664 Tar&Grv Plywood Plywood BrkCmn 113 TA Gd
## 666 CompShg CemntBd Wd Sdng None 0 TA TA
## 670 CompShg Plywood Plywood None 0 Gd TA
## 677 CompShg Wd Sdng Wd Sdng BrkFace 57 TA TA
## 680 CompShg Wd Sdng Wd Sdng None 0 TA TA
## 683 CompShg HdBoard HdBoard None 0 Gd TA
## 685 CompShg MetalSd MetalSd None 0 Gd TA
## 688 CompShg VinylSd VinylSd BrkFace 147 Gd TA
## 704 CompShg Plywood Plywood Stone 351 TA TA
## 707 CompShg HdBoard HdBoard BrkCmn 41 TA TA
## 712 CompShg HdBoard Plywood BrkFace 289 Gd TA
## 718 CompShg HdBoard HdBoard None 0 Gd TA
## 719 CompShg VinylSd VinylSd BrkFace 169 Gd TA
## 724 CompShg Wd Sdng Plywood BrkFace 260 Gd Gd
## 732 CompShg Plywood Plywood None 0 TA TA
## 743 CompShg VinylSd VinylSd BrkFace 289 Ex Gd
## 744 CompShg VinylSd VinylSd None 0 Gd TA
## 749 CompShg VinylSd VinylSd None 0 Gd TA
## 755 CompShg HdBoard HdBoard BrkCmn 328 TA TA
## 768 CompShg Wd Sdng Wd Sdng None 0 TA TA
## 781 CompShg Plywood Plywood BrkFace 104 TA Gd
## 783 CompShg BrkFace Wd Sdng None 0 TA TA
## 787 CompShg HdBoard HdBoard BrkFace 157 TA TA
## 789 CompShg HdBoard HdBoard None 0 TA TA
## 792 CompShg MetalSd MetalSd None 0 Gd TA
## 809 CompShg VinylSd VinylSd BrkFace 169 Gd TA
## 814 CompShg BrkFace BrkFace None 0 TA TA
## 815 CompShg CemntBd CmentBd BrkFace 148 Gd TA
## 820 CompShg VinylSd VinylSd None 0 Gd TA
## 826 Tar&Grv Plywood Plywood None 0 TA TA
## 838 CompShg Wd Sdng Wd Sdng None 0 TA TA
## 843 CompShg HdBoard HdBoard None 0 TA TA
## 849 CompShg VinylSd VinylSd BrkFace 40 Gd TA
## 851 CompShg MetalSd HdBoard BrkFace 115 TA Gd
## 853 CompShg HdBoard HdBoard None 0 TA Gd
## 854 CompShg Plywood HdBoard None 0 TA TA
## 857 CompShg HdBoard HdBoard BrkFace 220 TA TA
## 863 CompShg MetalSd MetalSd BrkFace 76 TA TA
## 866 CompShg Plywood Plywood None 0 TA TA
## 877 CompShg VinylSd VinylSd BrkFace 90 Gd Gd
## 880 CompShg VinylSd VinylSd None 0 TA TA
## 891 CompShg Wd Sdng Plywood None 0 TA TA
## 898 CompShg HdBoard HdBoard None 0 TA TA
## 902 CompShg HdBoard Wd Sdng BrkFace 75 TA TA
## 906 CompShg HdBoard HdBoard None 0 TA TA
## 909 CompShg HdBoard Plywood None 0 TA TA
## 915 CompShg VinylSd VinylSd None 0 TA TA
## 923 CompShg VinylSd VinylSd None 0 TA TA
## 925 CompShg MetalSd MetalSd BrkFace 342 TA TA
## 926 CompShg VinylSd VinylSd None 0 Gd TA
## 927 CompShg HdBoard HdBoard BrkFace 285 TA TA
## 936 CompShg MetalSd MetalSd None 0 TA Gd
## 938 CompShg VinylSd VinylSd BrkFace 298 Gd TA
## 941 CompShg HdBoard HdBoard BrkFace 541 TA TA
## 950 CompShg HdBoard HdBoard BrkFace 232 TA TA
## 958 CompShg HdBoard HdBoard BrkFace 424 TA Gd
## 964 CompShg Wd Sdng Wd Sdng BrkFace 151 TA TA
## 971 CompShg MetalSd MetalSd None 0 Gd TA
## 975 CompShg CemntBd CmentBd Stone 210 Ex TA
## 978 CompShg CemntBd CmentBd None 0 Gd TA
## 983 CompShg Plywood Plywood BrkFace 298 TA TA
## 991 CompShg Wd Sdng Wd Sdng None 0 TA TA
## 992 CompShg HdBoard HdBoard BrkFace 571 TA TA
## 998 CompShg VinylSd VinylSd BrkFace 164 TA TA
## 1001 CompShg Plywood Plywood None 0 TA TA
## 1012 CompShg HdBoard HdBoard None 0 Gd TA
## 1013 CompShg HdBoard HdBoard BrkFace 76 Gd TA
## 1019 WdShake Stone HdBoard None 0 Gd TA
## 1025 CompShg VinylSd VinylSd None 0 TA TA
## 1027 CompShg MetalSd MetalSd None 0 Gd Gd
## 1028 CompShg VinylSd VinylSd Stone 295 Gd TA
## 1030 CompShg Wd Sdng Wd Sdng None 0 TA Gd
## 1032 CompShg VinylSd VinylSd BrkFace 396 Gd TA
## 1036 CompShg HdBoard HdBoard BrkFace 252 TA TA
## 1040 CompShg BrkFace Wd Sdng None 0 TA TA
## 1052 CompShg HdBoard HdBoard None 0 Gd TA
## 1054 CompShg MetalSd MetalSd BrkFace 480 TA TA
## 1059 CompShg Plywood Plywood BrkFace 200 TA TA
## 1072 CompShg VinylSd Plywood None 0 TA TA
## 1079 CompShg HdBoard HdBoard None 0 TA TA
## 1081 CompShg CemntBd CmentBd None 0 TA TA
## 1092 CompShg HdBoard HdBoard None 0 Gd TA
## 1103 CompShg VinylSd VinylSd None 0 TA TA
## 1105 CompShg HdBoard HdBoard None 0 TA TA
## 1111 CompShg VinylSd VinylSd None 0 Gd TA
## 1117 CompShg AsbShng AsbShng None 0 TA TA
## 1119 CompShg HdBoard HdBoard BrkFace 170 TA TA
## 1133 CompShg Plywood ImStucc None 0 TA TA
## 1136 CompShg Plywood Plywood BrkFace 44 TA Gd
## 1138 CompShg Wd Sdng Plywood None 0 TA TA
## 1141 CompShg Wd Sdng Wd Shng BrkFace 85 Gd TA
## 1143 CompShg Wd Sdng Wd Sdng None 0 TA TA
## 1148 CompShg Wd Sdng Wd Sdng None 0 Gd Gd
## 1149 CompShg VinylSd VinylSd Stone 288 TA TA
## 1156 CompShg HdBoard HdBoard BrkFace 72 Gd TA
## 1159 CompShg Plywood Plywood None 0 TA TA
## 1172 CompShg MetalSd MetalSd None 0 TA TA
## 1175 CompShg MetalSd MetalSd None 0 TA TA
## 1185 CompShg MetalSd MetalSd Stone 149 TA Gd
## 1188 CompShg VinylSd VinylSd BrkFace 425 TA TA
## 1201 CompShg HdBoard HdBoard None 0 TA TA
## 1208 CompShg VinylSd VinylSd None 0 TA Gd
## 1225 CompShg Plywood Plywood None 1 TA TA
## 1228 CompShg Plywood Plywood BrkFace 180 TA TA
## 1238 CompShg BrkFace Stucco None 0 TA TA
## 1241 CompShg HdBoard HdBoard BrkFace 335 TA TA
## 1245 CompShg VinylSd Wd Shng Stone 163 Gd TA
## 1247 CompShg Wd Sdng Wd Sdng None 0 Gd TA
## 1254 CompShg VinylSd VinylSd None 0 TA TA
## 1256 CompShg Wd Sdng Wd Sdng None 0 TA TA
## 1262 CompShg Stucco Stucco BrkFace 632 TA Gd
## 1264 WdShake Plywood Plywood None 0 Gd TA
## 1265 CompShg BrkFace BrkFace None 0 TA TA
## 1266 CompShg HdBoard Plywood BrkFace 114 TA TA
## 1270 CompShg HdBoard Plywood None 0 TA TA
## 1271 CompShg HdBoard HdBoard BrkFace 359 TA TA
## 1279 CompShg HdBoard HdBoard BrkFace 451 TA TA
## 1280 CompShg Wd Sdng Wd Sdng BrkCmn 621 TA Gd
## 1283 CompShg Wd Sdng HdBoard BrkFace 86 TA TA
## 1293 CompShg VinylSd VinylSd None 344 Gd TA
## 1294 CompShg Wd Sdng Wd Sdng None 0 TA TA
## 1302 CompShg HdBoard HdBoard BrkFace 88 TA TA
## 1305 CompShg Wd Sdng Wd Sdng BrkFace 336 Gd TA
## 1311 CompShg VinylSd VinylSd BrkFace 178 Gd TA
## 1314 CompShg VinylSd VinylSd None 0 TA TA
## 1335 CompShg VinylSd VinylSd BrkFace 149 Gd TA
## 1339 CompShg BrkFace HdBoard None 0 TA TA
## 1341 CompShg VinylSd VinylSd None 0 Gd TA
## 1347 CompShg VinylSd VinylSd None 0 Gd TA
## 1349 CompShg HdBoard HdBoard BrkFace 65 TA TA
## 1350 CompShg VinylSd VinylSd None 0 TA TA
## 1351 CompShg MetalSd MetalSd BrkFace 216 Gd TA
## 1355 CompShg VinylSd VinylSd None 0 TA Gd
## 1358 CompShg VinylSd VinylSd None 0 Gd TA
## 1361 CompShg VinylSd VinylSd BrkFace 170 Gd TA
## 1366 CompShg VinylSd VinylSd BrkFace 705 Ex TA
## 1373 CompShg BrkFace Plywood None 0 TA TA
## 1375 CompShg Wd Sdng Wd Sdng None 0 TA Gd
## 1388 CompShg Wd Sdng Wd Sdng None 0 TA TA
## 1399 CompShg HdBoard Plywood None 0 TA TA
## 1409 CompShg VinylSd VinylSd BrkFace 731 Gd TA
## 1411 CompShg Plywood Plywood BrkFace 312 Gd Gd
## 1415 Tar&Grv Plywood Plywood None 0 Gd Gd
## 1416 CompShg HdBoard HdBoard None 0 TA TA
## 1421 CompShg MetalSd MetalSd BrkFace 310 Gd Gd
## 1423 CompShg Plywood Plywood None 0 TA TA
## 1433 CompShg VinylSd VinylSd BrkFace 147 Gd TA
## 1435 CompShg Wd Sdng Wd Sdng None 0 TA TA
## 1438 CompShg HdBoard HdBoard BrkFace 189 TA TA
## Foundation BsmtQual BsmtCond BsmtExposure BsmtFinType1 BsmtFinSF1
## 8 CBlock Gd TA Mn ALQ 859
## 13 CBlock TA TA No ALQ 737
## 15 CBlock TA TA No BLQ 733
## 17 CBlock TA TA No ALQ 578
## 25 CBlock TA TA Mn Rec 188
## 32 CBlock TA TA No Unf 0
## 43 CBlock Gd TA Av ALQ 747
## 44 CBlock Gd TA Av LwQ 280
## 51 PConc Gd TA Av GLQ 182
## 65 PConc Gd TA No GLQ 739
## 67 CBlock Gd TA Gd GLQ 912
## 77 CBlock TA TA No ALQ 228
## 85 PConc Gd TA No Unf 0
## 96 PConc Gd Gd No ALQ 310
## 101 PConc TA TA Mn ALQ 1200
## 105 PConc TA TA No LwQ 224
## 112 PConc Gd TA No GLQ 250
## 114 CBlock Gd TA Mn ALQ 35
## 117 CBlock TA TA No LwQ 170
## 121 CBlock TA TA Gd ALQ 938
## 127 CBlock Gd TA No ALQ 120
## 132 PConc Gd TA No GLQ 695
## 134 PConc Ex TA No GLQ 1005
## 137 CBlock TA TA No BLQ 695
## 148 PConc Gd TA No Unf 0
## 150 BrkTil Gd TA No Unf 0
## 153 CBlock TA TA No Rec 416
## 154 CBlock Gd TA Gd BLQ 429
## 161 PConc Gd TA No BLQ 660
## 167 CBlock TA TA No LwQ 379
## 170 CBlock Gd TA No Unf 0
## 171 CBlock TA TA No Rec 360
## 178 CBlock TA TA No ALQ 57
## 181 PConc Gd TA No GLQ 436
## 187 PConc Gd TA Av GLQ 611
## 192 CBlock TA TA No ALQ 626
## 204 PConc Gd TA Av GLQ 662
## 208 PConc TA TA No ALQ 419
## 209 CBlock Gd TA Gd GLQ 1065
## 215 CBlock Gd TA No GLQ 378
## 219 CBlock TA TA No BLQ 341
## 222 PConc Gd TA No Unf 0
## 237 PConc Gd TA No BLQ 402
## 244 PConc Gd TA No GLQ 695
## 249 CBlock Gd TA Gd Rec 697
## 269 CBlock TA Gd No BLQ 751
## 287 CBlock TA TA No BLQ 614
## 288 CBlock TA TA No BLQ 450
## 293 CBlock TA TA No ALQ 795
## 307 CBlock TA TA No Unf 0
## 308 CBlock TA TA No BLQ 262
## 310 PConc Gd TA No ALQ 518
## 319 CBlock Gd TA Av GLQ 1036
## 328 BrkTil TA TA No Unf 0
## 330 CBlock TA TA No GLQ 40
## 335 CBlock TA TA Gd ALQ 1249
## 342 Slab <NA> <NA> <NA> <NA> 0
## 346 CBlock TA TA Mn BLQ 498
## 347 CBlock TA TA No BLQ 1270
## 351 PConc Gd TA Gd GLQ 1082
## 356 PConc Gd TA No GLQ 560
## 360 CBlock Gd TA Av GLQ 773
## 361 CBlock TA TA No Rec 399
## 364 PConc Gd TA Mn GLQ 712
## 366 CBlock Gd TA No BLQ 609
## 369 CBlock TA TA No ALQ 72
## 370 PConc Gd TA No Unf 0
## 375 BrkTil Fa Po Gd BLQ 350
## 384 PConc Gd TA Av GLQ 985
## 392 Slab <NA> <NA> <NA> <NA> 0
## 393 CBlock TA TA No Rec 266
## 404 PConc Gd TA No Unf 0
## 405 CBlock TA TA No BLQ 1116
## 412 PConc Ex TA Av GLQ 578
## 421 CBlock Gd TA No ALQ 1246
## 426 PConc Gd TA Gd GLQ 1518
## 447 PConc Gd TA No Unf 0
## 452 PConc Ex TA No ALQ 742
## 457 CBlock Gd TA Gd BLQ 1116
## 458 PConc TA TA No Unf 0
## 459 CBlock TA TA No LwQ 185
## 465 PConc Gd TA No Unf 0
## 470 PConc Gd TA Av GLQ 368
## 484 CBlock TA TA No ALQ 588
## 490 PConc Gd TA Mn Unf 0
## 496 PConc Gd TA No GLQ 1231
## 516 CBlock TA TA No Unf 0
## 518 PConc Gd TA Mn GLQ 706
## 536 CBlock TA TA No BLQ 600
## 537 CBlock TA TA No BLQ 673
## 538 PConc Gd TA Av GLQ 1358
## 540 PConc Gd TA No Unf 0
## 544 PConc Gd Gd No GLQ 1002
## 558 PConc Gd TA Gd Unf 0
## 559 CBlock Gd TA No ALQ 1302
## 563 PConc Gd TA No GLQ 728
## 568 CBlock Gd TA Gd GLQ 943
## 579 CBlock TA TA No BLQ 594
## 592 PConc Gd TA Av GLQ 685
## 609 PConc Ex TA Mn GLQ 904
## 610 CBlock Gd TA Av ALQ 605
## 611 PConc Gd TA Av GLQ 990
## 615 PConc Gd TA No GLQ 457
## 622 PConc Gd TA No GLQ 420
## 625 CBlock TA TA No Unf 0
## 640 PConc Gd TA No GLQ 738
## 644 CBlock TA TA No ALQ 282
## 658 CBlock Gd TA No Unf 0
## 664 CBlock Gd TA No LwQ 187
## 666 CBlock TA TA No Rec 988
## 670 CBlock Gd TA No ALQ 767
## 677 CBlock TA TA No Rec 827
## 680 PConc Gd TA Mn GLQ 1003
## 683 CBlock Gd TA Mn GLQ 828
## 685 PConc Gd TA No GLQ 239
## 688 PConc Gd TA Gd GLQ 697
## 704 CBlock Gd TA Gd GLQ 1219
## 707 PConc TA TA No Unf 0
## 712 CBlock TA TA No GLQ 533
## 718 PConc Gd TA Gd GLQ 1148
## 719 PConc Gd TA Av GLQ 662
## 724 CBlock Gd TA No GLQ 808
## 732 PConc TA TA No Unf 0
## 743 CBlock TA Gd No GLQ 575
## 744 PConc Gd TA No GLQ 300
## 749 PConc Gd TA No Unf 0
## 755 CBlock TA TA Mn Rec 438
## 768 CBlock Gd TA Av GLQ 685
## 781 PConc Gd TA Av GLQ 1097
## 783 CBlock TA TA No Rec 251
## 787 CBlock TA Fa Gd LwQ 568
## 789 PConc Gd TA Av ALQ 539
## 792 PConc Gd TA No Unf 0
## 809 PConc Gd TA Gd GLQ 662
## 814 CBlock TA TA No BLQ 486
## 815 PConc Gd TA No GLQ 1218
## 820 PConc Gd TA Gd Unf 0
## 826 PConc TA Gd Gd LwQ 249
## 838 BrkTil TA TA No BLQ 156
## 843 CBlock Gd TA Gd ALQ 1390
## 849 PConc Gd TA Gd Unf 0
## 851 CBlock TA TA Gd Rec 564
## 853 CBlock TA TA No ALQ 659
## 854 CBlock Gd Gd Gd GLQ 505
## 857 CBlock TA TA Mn BLQ 619
## 863 CBlock TA TA No BLQ 828
## 866 Slab <NA> <NA> <NA> <NA> 0
## 877 CBlock TA TA No ALQ 646
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## BsmtFinType2 BsmtFinSF2 BsmtUnfSF TotalBsmtSF Heating HeatingQC CentralAir
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## 13 Unf 0 175 912 GasA TA Y
## 15 Unf 0 520 1253 GasA TA Y
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## 25 ALQ 668 204 1060 GasA Ex Y
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## 134 Unf 0 262 1267 GasA Ex Y
## 137 Unf 0 519 1214 GasA TA Y
## 148 Unf 0 884 884 GasA Ex Y
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## 222 Unf 0 1010 1010 GasA Ex Y
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## 244 Unf 0 253 948 GasA Ex Y
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## 288 Unf 0 432 882 GasA TA Y
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## 518 Unf 0 88 794 GasA Ex Y
## 536 Unf 0 264 864 GasA TA Y
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## 1141 Unf 0 40 1298 GasA TA Y
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## 1314 <NA> 0 0 0 Floor TA N
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## 1350 Unf 0 344 1078 GasA Ex Y
## 1351 Unf 0 378 756 GasA Ex Y
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## 1361 Unf 0 163 848 GasA Ex Y
## 1366 Unf 0 1351 2633 GasA Ex Y
## 1373 Unf 0 340 1205 GasA Ex Y
## 1375 Unf 0 816 816 GasA Ex Y
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## 1409 Unf 0 503 1284 GasA Ex Y
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## 1421 Unf 0 762 1440 GasA Ex Y
## 1423 Unf 0 0 958 GasA TA Y
## 1433 Unf 0 151 848 GasA Ex Y
## 1435 Unf 0 952 952 Grav Fa N
## 1438 Unf 0 595 1188 GasA TA Y
## Electrical X1stFlrSF X2ndFlrSF LowQualFinSF GrLivArea BsmtFullBath
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## 1052 SBrkr 979 871 0 1850 0
## 1054 SBrkr 1096 895 0 1991 0
## 1059 SBrkr 1154 0 0 1154 0
## 1072 SBrkr 1096 0 0 1096 1
## 1079 SBrkr 691 807 0 1498 0
## 1081 SBrkr 546 546 0 1092 0
## 1092 SBrkr 1088 0 0 1088 0
## 1103 SBrkr 948 742 0 1690 0
## 1105 SBrkr 773 885 0 1658 1
## 1111 SBrkr 779 640 0 1419 1
## 1117 FuseA 960 0 0 960 0
## 1119 SBrkr 812 670 0 1482 0
## 1133 SBrkr 1567 0 0 1567 1
## 1136 SBrkr 1088 780 0 1868 1
## 1138 SBrkr 1006 0 0 1006 0
## 1141 SBrkr 1298 0 0 1298 1
## 1143 SBrkr 572 539 0 1111 0
## 1148 SBrkr 816 0 0 816 0
## 1149 SBrkr 902 918 0 1820 0
## 1156 SBrkr 1640 0 0 1640 1
## 1159 SBrkr 1432 0 0 1432 1
## 1172 SBrkr 818 406 0 1224 0
## 1175 SBrkr 1298 1216 0 2514 0
## 1185 SBrkr 1622 0 0 1622 1
## 1188 SBrkr 1224 0 0 1224 1
## 1201 SBrkr 1056 0 0 1056 1
## 1208 SBrkr 960 0 0 960 1
## 1225 SBrkr 1361 1259 0 2620 0
## 1228 SBrkr 1188 0 0 1188 1
## 1238 SBrkr 792 725 0 1517 0
## 1241 SBrkr 1034 0 0 1034 1
## 1245 SBrkr 1405 0 0 1405 0
## 1247 SBrkr 1516 651 0 2167 1
## 1254 SBrkr 892 783 0 1675 0
## 1256 FuseA 1104 684 0 1788 1
## 1262 SBrkr 1968 1479 0 3447 0
## 1264 SBrkr 1332 192 0 1524 2
## 1265 SBrkr 1489 0 0 1489 0
## 1266 SBrkr 935 0 0 935 1
## 1270 SBrkr 735 660 0 1395 0
## 1271 SBrkr 1724 0 0 1724 1
## 1279 SBrkr 1328 0 0 1328 1
## 1280 SBrkr 1582 0 0 1582 0
## 1283 SBrkr 1152 0 0 1152 1
## 1293 SBrkr 1005 978 0 1983 0
## 1294 SBrkr 753 741 0 1494 0
## 1302 SBrkr 1294 0 0 1294 1
## 1305 SBrkr 1453 1357 0 2810 0
## 1311 SBrkr 1787 0 0 1787 0
## 1314 SBrkr 720 0 0 720 0
## 1335 SBrkr 1284 885 0 2169 0
## 1339 SBrkr 2156 0 0 2156 0
## 1341 SBrkr 1494 0 0 1494 1
## 1347 SBrkr 992 873 0 1865 1
## 1349 SBrkr 892 0 0 892 0
## 1350 SBrkr 1078 0 0 1078 1
## 1351 SBrkr 769 804 0 1573 0
## 1355 SBrkr 1281 457 0 1738 0
## 1358 SBrkr 814 860 0 1674 1
## 1361 SBrkr 848 0 0 848 1
## 1366 SBrkr 2633 0 0 2633 1
## 1373 SBrkr 2117 0 0 2117 0
## 1375 SBrkr 1416 0 0 1416 0
## 1388 SBrkr 1687 0 0 1687 1
## 1399 SBrkr 833 0 0 833 1
## 1409 SBrkr 1310 1140 0 2450 1
## 1411 SBrkr 1844 0 0 1844 1
## 1415 SBrkr 1575 626 0 2201 0
## 1416 SBrkr 1344 0 0 1344 1
## 1421 SBrkr 1440 0 0 1440 0
## 1423 SBrkr 958 0 0 958 0
## 1433 SBrkr 848 0 0 848 1
## 1435 FuseF 952 0 0 952 0
## 1438 SBrkr 1188 0 0 1188 0
## BsmtHalfBath FullBath HalfBath BedroomAbvGr KitchenAbvGr KitchenQual
## 8 0 2 1 3 1 TA
## 13 0 1 0 2 1 TA
## 15 0 1 1 2 1 TA
## 17 0 1 0 2 1 TA
## 25 0 1 0 3 1 Gd
## 32 0 1 1 3 1 Gd
## 43 0 1 0 2 1 Gd
## 44 0 1 0 3 1 TA
## 51 1 2 0 3 1 TA
## 65 0 2 1 3 1 Gd
## 67 0 2 0 3 1 TA
## 77 0 1 0 2 1 TA
## 85 0 2 1 3 1 TA
## 96 0 2 1 3 1 TA
## 101 0 2 0 3 1 Gd
## 105 0 1 1 4 1 TA
## 112 0 2 1 3 1 TA
## 114 0 2 0 3 1 Gd
## 117 1 1 0 3 1 TA
## 121 0 1 0 1 1 TA
## 127 0 2 0 2 1 TA
## 132 0 2 1 3 1 Gd
## 134 0 2 0 2 1 Gd
## 137 0 2 0 3 1 TA
## 148 0 2 1 3 1 Gd
## 150 0 1 0 3 1 TA
## 153 0 2 1 4 1 TA
## 154 0 1 0 1 1 TA
## 161 0 2 0 3 1 TA
## 167 0 1 0 2 1 TA
## 170 0 2 1 2 1 TA
## 171 0 1 1 2 1 TA
## 178 0 2 0 4 1 Gd
## 181 0 2 1 3 1 Gd
## 187 0 2 0 3 1 Gd
## 192 0 1 1 4 1 Gd
## 204 0 1 0 1 1 Gd
## 208 0 1 0 3 1 TA
## 209 0 2 1 3 1 TA
## 215 0 1 1 3 1 TA
## 219 1 1 1 3 1 Gd
## 222 0 2 1 4 1 Gd
## 237 0 2 1 3 1 Gd
## 244 0 2 1 3 1 Gd
## 249 1 2 0 4 1 Gd
## 269 0 1 1 3 1 TA
## 287 0 1 0 3 1 TA
## 288 0 1 0 3 1 TA
## 293 0 2 1 3 1 Gd
## 307 0 1 0 3 1 Fa
## 308 0 1 0 1 1 TA
## 310 0 2 1 3 1 Gd
## 319 0 2 0 3 1 TA
## 328 0 2 0 5 1 Gd
## 330 1 2 0 6 2 TA
## 335 0 2 0 3 1 TA
## 342 0 2 0 2 2 TA
## 346 0 1 0 2 1 TA
## 347 0 2 0 3 1 TA
## 351 0 1 1 2 1 Gd
## 356 0 2 0 3 1 Gd
## 360 0 1 0 2 1 TA
## 361 0 1 0 3 1 TA
## 364 0 2 1 3 1 TA
## 366 0 1 1 3 1 TA
## 369 0 1 0 3 1 Gd
## 370 0 2 1 3 1 TA
## 375 0 0 1 1 1 Fa
## 384 0 2 1 3 1 Gd
## 392 0 1 0 3 1 TA
## 393 0 1 0 2 1 TA
## 404 0 2 1 3 1 TA
## 405 0 2 0 3 1 TA
## 412 0 2 1 2 1 Gd
## 421 1 2 0 3 1 Gd
## 426 1 2 0 2 1 Gd
## 447 0 2 1 4 1 Gd
## 452 0 2 1 3 1 TA
## 457 0 1 0 2 1 Gd
## 458 0 1 0 3 1 Gd
## 459 0 1 0 3 1 Gd
## 465 0 2 0 2 1 Gd
## 470 0 1 1 1 1 Gd
## 484 0 1 0 3 1 Gd
## 490 0 1 1 3 1 TA
## 496 0 3 0 4 1 Gd
## 516 0 2 1 3 1 TA
## 518 0 2 1 3 1 TA
## 536 0 1 0 3 1 TA
## 537 0 1 1 3 1 TA
## 538 0 2 0 3 1 Gd
## 540 0 2 1 4 1 Gd
## 544 0 2 1 4 1 Gd
## 558 0 2 0 2 1 Gd
## 559 0 1 1 3 1 TA
## 563 0 2 1 4 1 Gd
## 568 0 1 0 2 1 TA
## 579 1 1 0 3 1 Gd
## 592 0 1 0 1 1 Gd
## 609 0 2 1 3 1 Ex
## 610 1 2 0 3 1 TA
## 611 0 2 1 3 1 Ex
## 615 0 2 1 3 1 Gd
## 622 0 2 1 2 1 Gd
## 625 0 1 0 3 1 TA
## 640 0 2 1 3 1 Gd
## 644 0 1 1 3 1 TA
## 658 0 2 1 3 1 TA
## 664 0 2 1 3 1 TA
## 666 0 1 1 3 1 TA
## 670 0 1 1 3 1 TA
## 677 0 1 0 3 1 TA
## 680 0 1 0 2 1 Gd
## 683 0 2 0 3 1 Gd
## 685 0 2 1 2 1 Gd
## 688 0 1 0 1 1 Gd
## 704 0 2 0 2 1 Gd
## 707 0 1 0 3 1 TA
## 712 0 2 1 3 1 Gd
## 718 0 2 0 2 1 Gd
## 719 0 1 0 1 1 Gd
## 724 0 2 0 3 1 Gd
## 732 0 1 0 3 1 TA
## 743 1 3 1 4 1 Gd
## 744 0 2 1 4 1 Gd
## 749 0 2 1 3 1 Gd
## 755 0 2 1 3 1 TA
## 768 0 1 0 2 1 TA
## 781 0 1 0 1 1 Gd
## 783 0 2 0 3 1 TA
## 787 0 2 1 5 1 Gd
## 789 0 1 0 3 1 TA
## 792 0 2 1 3 1 Gd
## 809 0 1 0 1 1 Gd
## 814 0 1 0 2 1 TA
## 815 0 2 0 3 1 Gd
## 820 0 2 1 3 1 Gd
## 826 1 2 1 3 1 TA
## 838 0 2 0 4 1 Fa
## 843 0 2 0 3 1 TA
## 849 0 2 0 2 1 Gd
## 851 0 1 1 3 1 TA
## 853 0 1 1 3 1 TA
## 854 0 1 0 3 1 TA
## 857 0 2 1 4 1 Gd
## 863 0 1 0 3 1 TA
## 866 0 2 0 2 1 TA
## 877 0 1 0 3 1 TA
## 880 0 2 1 3 1 TA
## 891 0 1 0 3 1 TA
## 898 0 1 0 2 1 TA
## 902 0 1 0 3 1 TA
## 906 0 1 0 2 1 TA
## 909 0 1 1 3 1 Gd
## 915 0 1 0 3 1 TA
## 923 1 2 0 3 1 Ex
## 925 0 2 1 4 1 TA
## 926 0 2 0 3 1 Ex
## 927 0 2 1 4 1 Gd
## 936 0 2 0 4 1 TA
## 938 0 2 1 3 1 Gd
## 941 1 1 0 3 1 Gd
## 950 1 2 1 4 1 TA
## 958 0 2 1 4 1 TA
## 964 0 1 0 3 1 TA
## 971 0 2 1 3 1 Gd
## 975 0 1 0 3 1 Ex
## 978 0 2 1 4 1 Gd
## 983 0 2 1 4 1 Gd
## 991 0 1 0 3 1 TA
## 992 0 2 0 2 1 TA
## 998 0 2 0 4 2 TA
## 1001 1 2 0 3 1 TA
## 1012 0 1 0 1 1 Gd
## 1013 0 2 1 3 1 Gd
## 1019 0 2 0 2 1 Gd
## 1025 0 2 0 5 2 TA
## 1027 0 2 1 5 1 Gd
## 1028 0 2 0 3 1 Gd
## 1030 0 1 0 3 1 TA
## 1032 0 2 1 3 1 Gd
## 1036 1 1 1 4 1 Gd
## 1040 0 2 0 4 1 TA
## 1052 0 2 1 3 1 Gd
## 1054 0 1 1 3 1 TA
## 1059 0 1 1 3 1 TA
## 1072 0 1 0 3 1 TA
## 1079 0 2 1 3 1 TA
## 1081 0 1 1 3 1 TA
## 1092 0 1 1 2 1 Gd
## 1103 0 2 1 3 1 TA
## 1105 0 2 1 3 1 TA
## 1111 0 2 1 3 1 Gd
## 1117 0 1 0 3 1 TA
## 1119 0 2 1 3 1 Gd
## 1133 0 2 0 2 1 Gd
## 1136 0 2 1 4 1 Gd
## 1138 0 1 0 3 1 TA
## 1141 0 2 0 3 1 Gd
## 1143 0 1 0 2 1 TA
## 1148 0 1 0 2 1 TA
## 1149 0 1 2 4 1 Gd
## 1156 0 1 0 3 1 Gd
## 1159 0 1 1 2 1 Gd
## 1172 0 1 0 3 1 TA
## 1175 0 2 1 4 1 TA
## 1185 0 1 0 3 1 TA
## 1188 0 2 0 2 1 TA
## 1201 0 1 0 2 1 TA
## 1208 1 0 0 0 1 TA
## 1225 0 2 2 4 2 TA
## 1228 0 1 0 3 1 TA
## 1238 0 1 0 3 1 Gd
## 1241 0 1 0 3 1 TA
## 1245 0 2 0 2 1 Gd
## 1247 0 2 1 3 1 Gd
## 1254 0 2 1 3 1 TA
## 1256 0 1 0 5 1 TA
## 1262 0 3 1 4 1 Gd
## 1264 0 0 1 0 1 Gd
## 1265 0 2 0 3 1 Gd
## 1266 0 1 0 3 1 TA
## 1270 1 1 1 3 1 TA
## 1271 0 1 1 3 1 TA
## 1279 0 1 1 3 1 TA
## 1280 1 2 0 4 1 TA
## 1283 0 1 0 3 1 TA
## 1293 0 2 1 3 1 Gd
## 1294 0 1 0 3 1 Gd
## 1302 0 2 0 3 1 Gd
## 1305 0 2 1 4 1 Gd
## 1311 0 2 0 3 1 Gd
## 1314 0 1 0 2 1 TA
## 1335 0 2 1 3 1 Gd
## 1339 0 2 0 3 1 TA
## 1341 0 2 0 3 1 Gd
## 1347 0 2 1 3 1 Gd
## 1349 0 1 0 3 1 TA
## 1350 0 1 1 3 1 TA
## 1351 0 2 1 3 1 Gd
## 1355 0 2 0 4 1 TA
## 1358 0 2 1 3 1 Gd
## 1361 0 1 0 1 1 Gd
## 1366 0 2 1 2 1 Ex
## 1373 0 2 1 4 1 TA
## 1375 0 2 0 3 1 Gd
## 1388 0 1 0 3 1 TA
## 1399 0 1 0 3 1 TA
## 1409 0 2 1 3 1 Gd
## 1411 0 2 0 3 1 Gd
## 1415 0 2 0 4 1 Gd
## 1416 0 1 0 2 1 TA
## 1421 0 2 0 3 1 Gd
## 1423 0 2 0 2 1 TA
## 1433 0 1 0 1 1 Gd
## 1435 0 1 0 2 1 Fa
## 1438 0 1 0 3 1 TA
## TotRmsAbvGrd Functional Fireplaces FireplaceQu GarageType GarageYrBlt
## 8 7 Typ 2 TA Attchd 1973
## 13 4 Typ 0 <NA> Detchd 1962
## 15 5 Typ 1 Fa Attchd 1960
## 17 5 Typ 1 TA Attchd 1970
## 25 6 Typ 1 TA Attchd 1968
## 32 6 Typ 0 <NA> Attchd 1966
## 43 5 Typ 0 <NA> Attchd 1983
## 44 5 Typ 0 <NA> Detchd 1977
## 51 6 Typ 0 <NA> Attchd 1997
## 65 8 Typ 0 <NA> Attchd 1998
## 67 7 Min1 1 Gd Attchd 1970
## 77 4 Typ 0 <NA> Detchd 1956
## 85 7 Typ 1 TA BuiltIn 1995
## 96 6 Typ 1 TA BuiltIn 1993
## 101 6 Typ 2 TA Attchd 1977
## 105 7 Typ 2 TA Detchd 1951
## 112 7 Typ 1 TA BuiltIn 2000
## 114 7 Typ 2 Gd Basment 1953
## 117 6 Typ 1 Po Attchd 1962
## 121 4 Typ 2 TA Attchd 1969
## 127 5 Typ 1 TA Attchd 1977
## 132 7 Typ 1 TA BuiltIn 2000
## 134 6 Typ 0 <NA> Attchd 2001
## 137 5 Typ 1 Fa Attchd 1967
## 148 8 Typ 1 Gd BuiltIn 2001
## 150 7 Typ 0 <NA> Detchd 1936
## 153 8 Typ 1 Gd Attchd 1971
## 154 4 Typ 1 Gd Attchd 1960
## 161 6 Typ 0 <NA> Attchd 1984
## 167 7 Typ 3 Gd Attchd 1955
## 170 6 Typ 1 TA Attchd 1981
## 171 7 Typ 0 <NA> Detchd 1991
## 178 8 Typ 1 Gd Attchd 1958
## 181 5 Typ 1 TA Detchd 2000
## 187 6 Typ 0 <NA> Attchd 1990
## 192 7 Typ 0 <NA> Attchd 1972
## 204 3 Typ 1 Gd Attchd 2004
## 208 6 Typ 1 Po Attchd 1960
## 209 7 Typ 1 Gd Attchd 1988
## 215 6 Typ 0 <NA> Attchd 1977
## 219 8 Typ 2 TA Attchd 1939
## 222 8 Typ 1 TA BuiltIn 2002
## 237 7 Typ 0 <NA> Attchd 1993
## 244 8 Typ 2 Fa Attchd 1994
## 249 7 Typ 2 TA Attchd 1958
## 269 6 Typ 1 Fa Attchd 1987
## 287 5 Typ 0 <NA> <NA> 0
## 288 5 Typ 0 <NA> Detchd 1970
## 293 9 Typ 1 TA Attchd 1977
## 307 6 Typ 0 <NA> <NA> 0
## 308 4 Typ 0 <NA> Detchd 1961
## 310 6 Typ 1 TA Attchd 1993
## 319 6 Typ 2 TA Attchd 1980
## 328 10 Typ 0 <NA> Detchd 1930
## 330 10 Typ 0 <NA> Detchd 2002
## 335 7 Typ 2 Gd Attchd 1965
## 342 6 Typ 0 <NA> Detchd 1949
## 346 5 Typ 0 <NA> Attchd 1960
## 347 6 Typ 2 Gd Attchd 1960
## 351 6 Typ 1 TA Attchd 1986
## 356 6 Typ 0 <NA> Attchd 1992
## 360 5 Typ 1 TA Attchd 1978
## 361 8 Typ 0 <NA> Detchd 1940
## 364 6 Typ 1 TA Attchd 1976
## 366 6 Typ 2 Gd Attchd 1963
## 369 7 Typ 1 Gd Detchd 1997
## 370 7 Typ 1 TA Attchd 2000
## 375 4 Maj1 0 <NA> <NA> 0
## 384 9 Typ 2 Fa Attchd 1992
## 392 5 Typ 0 <NA> Attchd 1959
## 393 4 Typ 2 TA <NA> 0
## 404 7 Typ 1 TA BuiltIn 1995
## 405 8 Min1 1 TA Attchd 1993
## 412 7 Typ 1 Gd Attchd 2009
## 421 8 Typ 1 TA Attchd 1977
## 426 5 Typ 1 TA Attchd 1989
## 447 10 Typ 1 TA Attchd 1998
## 452 7 Typ 0 <NA> Attchd 1996
## 457 6 Min1 2 Gd Attchd 1954
## 458 7 Typ 1 Gd Detchd 1925
## 459 5 Typ 1 TA Detchd 1950
## 465 6 Typ 1 TA Attchd 2004
## 470 4 Typ 0 <NA> Attchd 1985
## 484 6 Typ 0 <NA> Detchd 1963
## 490 4 Typ 1 Gd BuiltIn 1976
## 496 10 Typ 1 Gd Attchd 1992
## 516 7 Typ 1 TA Attchd 1972
## 518 7 Typ 0 <NA> Attchd 1998
## 536 5 Typ 0 <NA> Detchd 1980
## 537 7 Typ 1 Fa Attchd 1968
## 538 6 Typ 1 TA Attchd 2001
## 540 8 Typ 1 TA BuiltIn 2000
## 544 9 Typ 0 <NA> Attchd 1988
## 558 7 Typ 1 TA Attchd 2003
## 559 5 Mod 1 Gd Detchd 1957
## 563 9 Typ 1 TA Attchd 1992
## 568 4 Typ 2 TA Detchd 1979
## 579 7 Typ 2 Gd Attchd 1960
## 592 4 Typ 0 <NA> Attchd 2003
## 609 8 Typ 2 Ex Attchd 2000
## 610 6 Typ 1 TA Attchd 1978
## 611 7 Typ 1 TA BuiltIn 2001
## 615 7 Typ 1 Gd Attchd 2002
## 622 4 Typ 1 TA Detchd 2000
## 625 6 Min1 1 TA Attchd 1960
## 640 7 Typ 1 TA Attchd 2001
## 644 5 Typ 0 <NA> Detchd 1979
## 658 7 Typ 1 TA Attchd 1976
## 664 7 Sev 1 Po CarPort 1965
## 666 6 Min2 1 TA Detchd 1999
## 670 6 Typ 1 TA Attchd 1977
## 677 5 Typ 0 <NA> Detchd 1963
## 680 6 Typ 1 Gd Attchd 1996
## 683 6 Min1 1 TA Attchd 1984
## 685 4 Typ 0 <NA> Detchd 2004
## 688 3 Typ 1 TA Attchd 2004
## 704 5 Typ 2 TA Attchd 1971
## 707 6 Typ 0 <NA> Attchd 1966
## 712 6 Typ 0 <NA> Attchd 1976
## 718 5 Typ 1 TA Attchd 1985
## 719 3 Typ 0 <NA> Attchd 2004
## 724 5 Typ 1 Gd Attchd 1988
## 732 6 Typ 0 <NA> Attchd 1968
## 743 11 Typ 2 TA BuiltIn 1994
## 744 8 Typ 1 TA BuiltIn 2000
## 749 6 Typ 0 <NA> Attchd 2003
## 755 6 Typ 1 TA Attchd 1978
## 768 5 Typ 0 <NA> Detchd 1983
## 781 4 Typ 1 TA Attchd 1978
## 783 7 Typ 1 Gd Attchd 1967
## 787 9 Typ 0 <NA> Attchd 1966
## 789 5 Typ 2 TA Attchd 1976
## 792 7 Typ 1 TA Attchd 1994
## 809 4 Typ 1 Gd Attchd 2004
## 814 4 Typ 1 Gd Attchd 1954
## 815 7 Typ 2 Gd Attchd 2002
## 820 7 Typ 1 Gd BuiltIn 2003
## 826 7 Min2 0 <NA> Attchd 1967
## 838 7 Typ 0 <NA> Detchd 1934
## 843 6 Min2 2 TA Basment 1975
## 849 7 Typ 1 TA Attchd 2003
## 851 7 Typ 1 Fa Attchd 1964
## 853 6 Typ 0 <NA> Detchd 1962
## 854 5 Typ 0 <NA> Detchd 1981
## 857 9 Typ 1 Gd Attchd 1968
## 863 5 Typ 0 <NA> Detchd 1973
## 866 7 Typ 1 TA Attchd 1979
## 877 6 Typ 0 <NA> Attchd 1978
## 880 7 Typ 1 TA BuiltIn 1993
## 891 6 Typ 1 Gd Attchd 1954
## 898 4 Typ 0 <NA> Detchd 1979
## 902 6 Typ 0 <NA> Attchd 1967
## 906 5 Typ 0 <NA> Attchd 1983
## 909 5 Typ 0 <NA> Detchd 1978
## 915 6 Typ 0 <NA> Attchd 1956
## 923 6 Typ 0 <NA> Attchd 1977
## 925 9 Typ 1 Gd Attchd 1968
## 926 7 Typ 1 TA Attchd 2001
## 927 8 Typ 1 TA BuiltIn 1997
## 936 10 Typ 2 TA Attchd 1940
## 938 8 Typ 1 TA BuiltIn 1999
## 941 7 Typ 1 Gd Basment 1958
## 950 7 Min2 1 Po Attchd 1969
## 958 11 Typ 1 TA Attchd 1977
## 964 6 Typ 0 <NA> Attchd 1955
## 971 6 Typ 0 <NA> Detchd 2000
## 975 6 Typ 0 <NA> Attchd 1961
## 978 9 Typ 1 Gd Attchd 2002
## 983 8 Typ 1 TA Attchd 1976
## 991 6 Typ 0 <NA> Attchd 1961
## 992 6 Typ 1 TA Attchd 1970
## 998 8 Typ 0 <NA> Detchd 1976
## 1001 7 Typ 1 TA Attchd 1970
## 1012 4 Typ 1 Ex Attchd 1984
## 1013 7 Typ 1 TA Attchd 1991
## 1019 10 Typ 1 Gd Attchd 1976
## 1025 10 Typ 0 <NA> <NA> 0
## 1027 10 Typ 1 TA Attchd 1993
## 1028 6 Typ 0 <NA> Attchd 2002
## 1030 5 Typ 0 <NA> Detchd 1957
## 1032 7 Typ 1 TA BuiltIn 2001
## 1036 7 Typ 0 <NA> Attchd 1966
## 1040 8 Min2 1 Gd Attchd 1955
## 1052 7 Typ 1 Gd BuiltIn 1994
## 1054 7 Typ 1 Gd Detchd 1977
## 1059 6 Typ 1 Po Attchd 1966
## 1072 6 Typ 0 <NA> Attchd 1969
## 1079 6 Typ 1 TA Attchd 1995
## 1081 6 Typ 0 <NA> Attchd 1973
## 1092 5 Typ 0 <NA> Attchd 1987
## 1103 7 Typ 1 TA Attchd 2000
## 1105 8 Typ 1 TA Attchd 1995
## 1111 7 Typ 1 TA BuiltIn 2002
## 1117 5 Typ 0 <NA> Basment 1956
## 1119 7 Typ 1 TA Attchd 1992
## 1133 5 Typ 2 TA Attchd 1977
## 1136 9 Typ 1 TA Attchd 1976
## 1138 5 Typ 0 <NA> <NA> 0
## 1141 5 Typ 1 TA Attchd 1985
## 1143 5 Typ 1 Gd Detchd 1982
## 1148 5 Typ 0 <NA> Detchd 2002
## 1149 8 Typ 2 Gd Attchd 1965
## 1156 7 Typ 1 Gd Detchd 1993
## 1159 5 Typ 1 TA Attchd 1978
## 1172 5 Typ 0 <NA> Detchd 1926
## 1175 8 Typ 0 <NA> Attchd 1990
## 1185 7 Typ 1 TA 2Types 1975
## 1188 5 Typ 0 <NA> Attchd 1999
## 1201 5 Typ 0 <NA> Detchd 1966
## 1208 3 Typ 0 <NA> Attchd 1965
## 1225 12 Typ 1 TA BuiltIn 1977
## 1228 6 Typ 0 <NA> Attchd 1959
## 1238 7 Typ 2 Gd Detchd 1931
## 1241 6 Typ 0 <NA> Attchd 1976
## 1245 6 Typ 1 Gd Attchd 2003
## 1247 9 Typ 2 Gd Attchd 1974
## 1254 7 Typ 1 TA BuiltIn 1999
## 1256 8 Min2 2 TA Attchd 1957
## 1262 11 Typ 2 Gd BuiltIn 1982
## 1264 4 Typ 1 TA Attchd 1979
## 1265 7 Typ 1 Gd Attchd 1968
## 1266 5 Typ 0 <NA> Attchd 1965
## 1270 6 Typ 1 TA Attchd 1972
## 1271 7 Typ 1 Gd Attchd 1967
## 1279 6 Typ 2 Gd Attchd 1963
## 1280 7 Typ 0 <NA> Attchd 1964
## 1283 6 Typ 1 Gd Attchd 1964
## 1293 9 Typ 1 TA Attchd 1999
## 1294 7 Typ 2 Gd Attchd 1942
## 1302 6 Typ 0 <NA> Attchd 1991
## 1305 9 Typ 1 Ex Attchd 1990
## 1311 7 Typ 1 TA Attchd 2001
## 1314 4 Typ 0 <NA> Detchd 1955
## 1335 7 Typ 1 Gd Attchd 2002
## 1339 9 Typ 1 Gd Attchd 1968
## 1341 5 Typ 1 Fa Attchd 1998
## 1347 7 Typ 1 TA Attchd 2000
## 1349 5 Typ 0 <NA> Attchd 1966
## 1350 6 Typ 1 Fa Attchd 1971
## 1351 5 Typ 0 <NA> Detchd 2000
## 1355 7 Typ 1 Gd Attchd 1920
## 1358 7 Typ 0 <NA> Attchd 2000
## 1361 4 Typ 0 <NA> Attchd 2003
## 1366 8 Typ 2 Gd Attchd 2001
## 1373 7 Typ 2 Gd Attchd 1970
## 1375 7 Typ 0 <NA> Attchd 2007
## 1388 7 Min1 2 TA Detchd 1966
## 1399 5 Typ 0 <NA> <NA> 0
## 1409 7 Typ 1 TA Attchd 1998
## 1411 7 Typ 1 TA Attchd 1969
## 1415 8 Typ 1 Gd Attchd 1966
## 1416 6 Min1 1 TA Detchd 1970
## 1421 7 Typ 1 TA Attchd 1981
## 1423 5 Typ 0 <NA> Attchd 1976
## 1433 3 Typ 1 TA Attchd 2004
## 1435 4 Typ 1 Gd Detchd 1916
## 1438 6 Typ 0 <NA> Attchd 1962
## GarageFinish GarageCars GarageArea GarageQual GarageCond PavedDrive
## 8 RFn 2 484 TA TA Y
## 13 Unf 1 352 TA TA Y
## 15 RFn 1 352 TA TA Y
## 17 Fin 2 480 TA TA Y
## 25 Unf 1 270 TA TA Y
## 32 Unf 1 271 TA TA Y
## 43 RFn 2 504 TA Gd Y
## 44 Unf 1 308 TA TA Y
## 51 Fin 2 388 TA TA Y
## 65 RFn 2 645 TA TA Y
## 67 RFn 2 576 TA TA Y
## 77 Unf 1 283 TA TA Y
## 85 Fin 2 400 TA TA Y
## 96 Fin 2 420 TA TA Y
## 101 RFn 2 480 TA TA Y
## 105 Unf 1 240 TA TA Y
## 112 Fin 2 400 TA TA Y
## 114 Unf 2 450 TA TA Y
## 117 Unf 1 288 TA TA Y
## 121 Unf 2 540 TA TA Y
## 127 RFn 2 440 TA TA Y
## 132 RFn 2 390 TA TA Y
## 134 Fin 2 471 TA TA Y
## 137 RFn 1 318 TA TA Y
## 148 Fin 2 434 TA TA Y
## 150 Unf 1 240 Fa TA Y
## 153 RFn 2 495 TA TA Y
## 154 RFn 2 564 TA TA Y
## 161 Unf 2 516 TA TA Y
## 167 Fin 1 303 TA TA Y
## 170 RFn 2 511 TA TA Y
## 171 Unf 2 660 TA TA Y
## 178 Unf 2 451 TA TA Y
## 181 Unf 2 440 TA TA Y
## 187 Unf 2 497 TA TA Y
## 192 Fin 2 484 TA TA Y
## 204 RFn 2 420 TA TA Y
## 208 RFn 1 312 TA TA Y
## 209 Fin 2 454 TA TA Y
## 215 Fin 1 299 TA TA Y
## 219 Unf 2 431 TA TA Y
## 222 RFn 2 390 TA TA Y
## 237 RFn 2 457 TA TA Y
## 244 RFn 2 463 TA TA Y
## 249 Fin 2 389 TA TA Y
## 269 RFn 1 504 TA Gd Y
## 287 <NA> 0 0 <NA> <NA> Y
## 288 Unf 1 280 TA TA Y
## 293 Fin 2 539 TA TA Y
## 307 <NA> 0 0 <NA> <NA> N
## 308 Unf 2 539 TA TA Y
## 310 Fin 2 420 TA TA Y
## 319 Unf 2 588 TA TA Y
## 328 Unf 2 441 TA TA Y
## 330 Unf 1 352 TA TA Y
## 335 Fin 2 529 TA TA Y
## 342 Unf 2 400 TA TA Y
## 346 RFn 1 301 TA TA Y
## 347 Unf 2 498 TA TA Y
## 351 RFn 2 445 TA TA Y
## 356 RFn 2 400 TA TA Y
## 360 RFn 2 470 TA TA Y
## 361 Unf 1 240 TA TA N
## 364 Fin 2 566 TA TA Y
## 366 RFn 2 514 TA TA Y
## 369 Fin 2 576 TA TA Y
## 370 RFn 2 460 TA TA Y
## 375 <NA> 0 0 <NA> <NA> Y
## 384 Fin 2 501 TA TA Y
## 392 RFn 1 294 TA TA Y
## 393 <NA> 0 0 <NA> <NA> Y
## 404 Fin 2 373 TA TA Y
## 405 Unf 2 490 TA TA Y
## 412 Fin 2 484 TA TA Y
## 421 Fin 2 529 TA TA Y
## 426 Fin 2 569 TA TA Y
## 447 Fin 2 431 TA TA Y
## 452 Fin 2 422 TA TA Y
## 457 Fin 2 529 TA TA Y
## 458 Unf 1 228 TA TA Y
## 459 Unf 1 352 TA TA Y
## 465 Fin 2 398 TA TA Y
## 470 RFn 2 528 TA TA Y
## 484 Unf 1 264 TA TA Y
## 490 Fin 1 336 TA TA Y
## 496 RFn 2 546 TA TA Y
## 516 RFn 2 583 TA TA Y
## 518 RFn 2 546 TA TA Y
## 536 Unf 2 576 TA TA Y
## 537 Unf 1 336 TA TA Y
## 538 RFn 2 670 TA TA Y
## 540 Fin 3 648 TA TA Y
## 544 Unf 3 786 TA TA Y
## 558 Fin 2 420 TA TA Y
## 559 Unf 2 528 TA TA Y
## 563 RFn 2 590 TA TA Y
## 568 Unf 2 600 TA TA Y
## 579 Unf 2 572 TA TA Y
## 592 Fin 2 420 TA TA Y
## 609 Fin 3 736 TA TA Y
## 610 Unf 2 564 TA TA Y
## 611 RFn 2 531 TA TA Y
## 615 Fin 2 393 TA TA Y
## 622 Unf 2 440 TA TA Y
## 625 RFn 1 286 TA TA Y
## 640 Fin 2 650 TA TA Y
## 644 Unf 2 576 TA TA Y
## 658 RFn 2 550 TA TA Y
## 664 Unf 2 596 TA TA Y
## 666 Unf 2 576 TA TA Y
## 670 RFn 2 546 TA TA Y
## 677 Unf 2 572 TA TA Y
## 680 Unf 2 431 TA TA Y
## 683 Fin 2 577 TA TA Y
## 685 Unf 2 480 TA TA Y
## 688 RFn 2 420 TA TA Y
## 704 Unf 2 739 TA TA Y
## 707 Unf 1 408 TA TA Y
## 712 RFn 2 475 TA TA Y
## 718 RFn 2 564 TA TA Y
## 719 RFn 2 420 TA TA Y
## 724 Fin 2 540 TA TA Y
## 732 Unf 1 300 TA TA Y
## 743 Fin 2 831 TA TA Y
## 744 Fin 2 554 TA TA Y
## 749 Fin 2 400 TA TA Y
## 755 Fin 2 440 TA TA Y
## 768 Unf 2 576 TA TA Y
## 781 Fin 2 602 TA TA Y
## 783 Unf 2 457 TA TA Y
## 787 Fin 2 444 TA TA Y
## 789 RFn 2 539 TA TA Y
## 792 Fin 2 409 TA TA Y
## 809 Fin 2 420 TA TA Y
## 814 RFn 1 275 TA TA Y
## 815 RFn 3 857 TA TA Y
## 820 Fin 2 433 TA TA Y
## 826 Fin 2 538 TA TA Y
## 838 Unf 1 400 TA TA P
## 843 Fin 2 611 TA TA Y
## 849 Fin 2 400 TA TA Y
## 851 RFn 2 645 TA TA Y
## 853 Unf 1 260 TA TA Y
## 854 Unf 2 576 TA Fa Y
## 857 Unf 2 619 TA TA Y
## 863 Unf 2 902 TA TA Y
## 866 Unf 2 672 TA TA P
## 877 Unf 1 336 TA TA Y
## 880 RFn 2 389 TA TA Y
## 891 Unf 1 354 TA TA Y
## 898 Unf 1 684 TA TA Y
## 902 Unf 1 288 TA TA Y
## 906 Unf 2 484 TA TA Y
## 909 Unf 1 252 TA TA Y
## 915 RFn 1 284 TA TA Y
## 923 RFn 2 540 TA TA Y
## 925 RFn 2 486 TA TA Y
## 926 RFn 2 522 TA TA Y
## 927 Fin 2 642 TA TA Y
## 936 Unf 1 349 TA TA Y
## 938 RFn 2 390 TA TA Y
## 941 RFn 2 525 TA TA Y
## 950 RFn 2 530 TA TA Y
## 958 Fin 2 619 TA TA Y
## 964 Unf 1 260 TA TA Y
## 971 Unf 2 490 TA TA Y
## 975 RFn 2 588 TA TA Y
## 978 Unf 2 779 TA TA Y
## 983 Fin 2 551 TA TA Y
## 991 Unf 1 368 TA TA Y
## 992 RFn 2 615 TA TA Y
## 998 Unf 2 528 TA TA Y
## 1001 Unf 2 484 TA TA Y
## 1012 RFn 2 565 TA TA Y
## 1013 RFn 2 402 TA TA Y
## 1019 Fin 2 665 TA TA Y
## 1025 <NA> 0 0 <NA> <NA> N
## 1027 RFn 3 796 TA TA Y
## 1028 Unf 3 900 TA TA Y
## 1030 Unf 1 290 TA TA N
## 1032 Fin 2 905 TA TA Y
## 1036 Unf 2 484 TA TA Y
## 1040 Unf 2 452 TA TA Y
## 1052 Fin 2 467 TA TA Y
## 1054 Unf 2 432 TA Fa Y
## 1059 RFn 2 480 TA TA Y
## 1072 Fin 1 299 TA TA Y
## 1079 Fin 2 409 TA TA Y
## 1081 RFn 1 286 TA TA Y
## 1092 RFn 2 461 TA TA Y
## 1103 RFn 2 463 TA TA Y
## 1105 Fin 2 431 TA TA Y
## 1111 Fin 2 527 TA TA Y
## 1117 Unf 1 288 TA TA Y
## 1119 Fin 2 392 TA TA Y
## 1133 RFn 2 714 TA TA Y
## 1136 Unf 2 484 TA TA Y
## 1138 <NA> 0 0 <NA> <NA> Y
## 1141 Unf 2 403 TA TA Y
## 1143 Unf 1 288 TA TA Y
## 1148 Unf 1 432 TA TA Y
## 1149 Unf 2 492 TA TA Y
## 1156 Unf 2 924 TA TA Y
## 1159 Unf 2 588 TA TA Y
## 1172 Unf 1 210 TA TA N
## 1175 Fin 2 693 TA TA Y
## 1185 Fin 4 1356 TA TA Y
## 1188 Fin 2 402 TA TA Y
## 1201 Unf 1 384 TA TA Y
## 1208 Unf 1 364 TA TA Y
## 1225 RFn 2 600 TA TA N
## 1228 RFn 2 531 TA TA Y
## 1238 Unf 2 400 TA TA Y
## 1241 Unf 3 888 TA TA Y
## 1245 RFn 2 478 TA TA Y
## 1247 RFn 2 518 TA TA Y
## 1254 Fin 2 502 TA TA Y
## 1256 Unf 1 304 TA TA Y
## 1262 Unf 3 1014 TA TA Y
## 1264 Fin 2 586 TA TA Y
## 1265 RFn 2 462 TA TA Y
## 1266 Unf 1 288 TA TA Y
## 1270 Unf 2 497 TA TA Y
## 1271 RFn 2 480 TA TA Y
## 1279 Unf 2 528 TA TA Y
## 1280 Unf 2 390 TA TA N
## 1283 RFn 2 484 TA TA Y
## 1293 Fin 2 490 TA TA Y
## 1294 Unf 1 213 TA TA P
## 1302 RFn 2 496 TA TA Y
## 1305 RFn 2 750 Gd Gd Y
## 1311 RFn 3 748 TA TA Y
## 1314 Unf 1 287 TA Fa Y
## 1335 RFn 2 647 TA TA Y
## 1339 RFn 2 508 Gd TA Y
## 1341 RFn 2 514 TA TA Y
## 1347 RFn 3 839 TA TA Y
## 1349 RFn 1 264 TA TA Y
## 1350 Fin 2 500 TA TA Y
## 1351 Unf 2 440 TA TA Y
## 1355 Unf 1 368 TA TA Y
## 1358 RFn 2 663 TA TA Y
## 1361 Fin 2 420 TA TA Y
## 1366 RFn 3 804 TA TA Y
## 1373 Fin 2 550 TA TA Y
## 1375 Unf 2 576 TA TA N
## 1388 Unf 2 572 TA TA N
## 1399 <NA> 0 0 <NA> <NA> Y
## 1409 Fin 3 1069 TA TA Y
## 1411 RFn 2 540 TA TA Y
## 1415 Unf 2 432 Gd Gd Y
## 1416 Unf 1 484 TA TA Y
## 1421 Fin 2 467 TA TA Y
## 1423 RFn 2 440 TA TA Y
## 1433 RFn 2 420 TA TA Y
## 1435 Unf 1 192 Fa Po P
## 1438 Unf 1 312 TA TA P
## WoodDeckSF OpenPorchSF EnclosedPorch X3SsnPorch ScreenPorch PoolArea
## 8 235 204 228 0 0 0
## 13 140 0 0 0 176 0
## 15 0 213 176 0 0 0
## 17 0 0 0 0 0 0
## 25 406 90 0 0 0 0
## 32 0 65 0 0 0 0
## 43 240 0 0 0 0 0
## 44 145 0 0 0 0 0
## 51 0 75 0 0 0 0
## 65 576 36 0 0 0 0
## 67 301 0 0 0 0 0
## 77 0 0 0 0 0 0
## 85 120 72 0 0 0 0
## 96 232 63 0 0 0 0
## 101 168 68 0 0 0 0
## 105 0 0 0 0 184 0
## 112 180 0 0 0 0 0
## 114 166 120 192 0 0 0
## 117 0 20 144 0 0 0
## 121 0 130 0 130 0 0
## 127 0 205 0 0 0 0
## 132 24 48 0 0 0 0
## 134 192 25 0 0 0 0
## 137 0 111 0 0 0 0
## 148 144 48 0 0 0 0
## 150 200 114 0 0 0 0
## 153 0 66 0 0 0 0
## 154 409 0 0 0 0 0
## 161 0 0 0 0 0 0
## 167 476 0 0 0 142 0
## 170 574 64 0 0 0 0
## 171 237 0 0 0 0 0
## 178 0 0 0 0 0 0
## 181 0 0 0 0 0 0
## 187 168 27 0 0 0 0
## 192 0 32 0 0 0 0
## 204 149 0 0 0 0 0
## 208 355 0 0 0 0 0
## 209 60 55 0 0 154 0
## 215 0 36 0 0 0 0
## 219 0 119 150 0 0 0
## 222 120 46 0 0 0 0
## 237 370 70 0 238 0 0
## 244 0 130 0 0 0 0
## 249 0 98 0 0 0 0
## 269 370 30 0 0 0 0
## 287 0 0 0 0 0 0
## 288 0 0 0 0 0 0
## 293 0 250 0 0 0 0
## 307 0 144 0 0 0 0
## 308 158 0 0 0 0 0
## 310 190 63 0 0 0 0
## 319 233 48 0 0 0 0
## 328 0 60 268 0 0 0
## 330 155 0 0 0 0 0
## 335 670 0 0 0 0 0
## 342 0 0 0 0 0 0
## 346 0 0 0 0 0 0
## 347 0 40 0 0 0 0
## 351 0 80 0 0 184 0
## 356 120 26 0 0 0 0
## 360 0 0 0 0 192 0
## 361 0 0 0 0 0 0
## 364 306 111 0 0 0 0
## 366 0 76 0 0 185 0
## 369 364 17 0 0 182 0
## 370 100 40 0 0 0 0
## 375 0 0 0 0 0 0
## 384 216 231 0 0 0 0
## 392 0 0 0 0 0 0
## 393 0 0 0 0 0 0
## 404 0 40 0 0 0 0
## 405 120 78 0 0 0 0
## 412 0 144 0 0 0 0
## 421 240 0 0 0 0 0
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## 447 89 0 0 0 0 0
## 452 144 122 0 0 0 0
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## 458 192 63 0 0 0 0
## 459 0 0 248 0 0 0
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## 470 0 54 0 0 140 0
## 484 0 132 0 0 0 0
## 490 141 24 0 0 0 0
## 496 264 75 291 0 0 0
## 516 0 104 0 0 0 0
## 518 0 36 0 0 0 0
## 536 216 0 0 0 0 0
## 537 466 0 0 0 0 0
## 538 180 0 0 0 0 0
## 540 0 56 0 0 0 0
## 544 0 0 0 0 0 0
## 558 143 20 0 0 0 0
## 559 0 0 0 0 95 0
## 563 0 40 0 0 0 0
## 568 42 0 0 0 0 0
## 579 216 110 0 0 0 0
## 592 140 0 0 0 0 0
## 609 253 142 0 0 0 0
## 610 0 0 0 0 0 0
## 611 160 122 0 0 0 0
## 615 100 75 0 0 0 0
## 622 0 32 0 0 0 0
## 625 0 0 36 0 0 0
## 640 0 235 0 0 0 0
## 644 0 312 40 0 0 0
## 658 0 113 252 0 0 0
## 664 0 265 0 0 0 0
## 666 304 0 0 0 0 0
## 670 198 42 0 0 0 0
## 677 0 0 0 0 0 0
## 680 307 0 0 0 0 0
## 683 219 0 0 0 0 0
## 685 0 60 0 0 0 0
## 688 149 0 0 0 0 0
## 704 380 48 0 0 0 0
## 707 0 0 0 0 0 0
## 712 0 44 0 0 0 0
## 718 114 28 234 0 0 0
## 719 160 0 0 0 0 0
## 724 292 44 0 182 0 0
## 732 147 0 0 0 0 0
## 743 0 204 0 0 0 0
## 744 224 54 0 0 0 0
## 749 0 48 0 0 0 0
## 755 335 0 0 0 0 0
## 768 120 0 0 0 0 0
## 781 303 30 0 0 0 0
## 783 0 0 0 0 197 0
## 787 133 168 0 0 0 0
## 789 120 0 0 0 0 0
## 792 143 46 0 0 0 0
## 809 140 0 0 0 0 0
## 814 0 0 120 0 0 0
## 815 150 59 0 0 0 0
## 820 100 48 0 0 0 0
## 826 486 0 0 0 225 0
## 838 0 0 254 0 0 0
## 843 0 0 0 0 0 0
## 849 143 20 0 0 0 0
## 851 180 0 0 0 0 0
## 853 0 104 0 0 0 0
## 854 0 0 34 0 0 0
## 857 0 65 0 0 222 0
## 863 0 0 0 0 0 0
## 866 120 144 0 0 0 0
## 877 0 0 0 0 0 0
## 880 342 40 0 0 0 0
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## 898 0 0 0 0 0 0
## 902 0 0 0 0 0 0
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## 923 180 0 0 0 0 0
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## 927 0 0 0 0 0 0
## 936 56 0 318 0 0 0
## 938 0 0 0 168 0 0
## 941 0 118 0 0 233 0
## 950 305 189 0 0 0 0
## 958 550 282 0 0 0 0
## 964 0 0 0 0 0 0
## 971 153 50 0 0 0 0
## 975 144 76 0 0 0 0
## 978 0 0 0 0 0 0
## 983 0 224 0 0 0 0
## 991 0 319 0 0 0 0
## 992 371 0 0 0 0 0
## 998 0 0 0 0 0 0
## 1001 0 0 0 0 147 0
## 1012 63 0 0 0 0 0
## 1013 164 0 0 0 0 0
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## 1025 0 0 228 0 0 0
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## 1030 186 0 0 0 0 0
## 1032 0 45 0 0 189 0
## 1036 0 40 0 0 0 0
## 1040 0 0 0 0 0 0
## 1052 168 98 0 0 0 0
## 1054 0 0 19 0 0 0
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## 1072 240 32 0 0 0 0
## 1079 315 44 0 0 0 0
## 1081 120 96 0 0 0 0
## 1092 0 74 137 0 0 0
## 1103 100 48 0 0 0 0
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## 1111 120 0 0 0 0 0
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## 1119 100 25 0 0 0 0
## 1133 264 32 0 0 0 0
## 1136 448 96 0 0 0 0
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## 1141 165 26 0 0 0 0
## 1143 0 0 176 0 0 0
## 1148 0 0 96 0 0 0
## 1149 60 84 0 0 273 0
## 1156 108 0 0 216 0 0
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## 1175 0 0 0 0 0 0
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## 1238 0 0 0 0 0 0
## 1241 0 0 0 0 0 0
## 1245 148 36 0 0 0 0
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## 1256 120 0 0 0 0 0
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## 1264 268 0 0 0 0 0
## 1265 0 0 0 0 0 0
## 1266 180 0 0 0 0 0
## 1270 294 116 0 0 0 0
## 1271 0 0 0 0 0 0
## 1279 0 26 0 0 0 0
## 1280 168 198 0 0 0 0
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## 1293 0 0 0 0 0 0
## 1294 0 0 0 0 224 0
## 1302 112 51 0 0 0 0
## 1305 500 0 0 0 0 0
## 1311 198 150 0 0 0 0
## 1314 0 0 0 0 0 0
## 1335 192 87 0 0 0 0
## 1339 0 80 0 290 0 0
## 1341 402 25 0 0 0 0
## 1347 0 184 0 0 0 0
## 1349 0 0 0 0 0 0
## 1350 0 0 0 0 0 0
## 1351 0 32 0 0 0 0
## 1355 55 0 0 0 0 0
## 1358 0 96 0 0 0 0
## 1361 140 0 0 0 0 0
## 1366 314 140 0 0 0 0
## 1373 0 42 0 0 0 0
## 1375 0 0 112 0 0 0
## 1388 0 0 50 0 0 0
## 1399 0 0 0 0 0 0
## 1409 0 126 0 0 0 0
## 1411 0 73 216 0 0 0
## 1415 586 236 0 0 0 738
## 1416 316 28 0 0 0 0
## 1421 0 0 99 0 0 0
## 1423 0 60 0 0 0 0
## 1433 149 0 0 0 0 0
## 1435 0 98 0 0 40 0
## 1438 261 39 0 0 0 0
## PoolQC Fence MiscFeature MiscVal MoSold YrSold SaleType SaleCondition
## 8 <NA> <NA> Shed 350 11 2009 WD Normal
## 13 <NA> <NA> <NA> 0 9 2008 WD Normal
## 15 <NA> GdWo <NA> 0 5 2008 WD Normal
## 17 <NA> <NA> Shed 700 3 2010 WD Normal
## 25 <NA> MnPrv <NA> 0 5 2010 WD Normal
## 32 <NA> MnPrv <NA> 0 6 2008 WD Normal
## 43 <NA> MnPrv <NA> 0 12 2007 WD Normal
## 44 <NA> MnPrv <NA> 0 7 2008 WD Normal
## 51 <NA> <NA> <NA> 0 7 2007 WD Normal
## 65 <NA> GdPrv <NA> 0 2 2009 WD Normal
## 67 <NA> <NA> <NA> 0 7 2010 WD Normal
## 77 <NA> <NA> <NA> 0 4 2008 WD Normal
## 85 <NA> <NA> Shed 700 5 2009 WD Normal
## 96 <NA> <NA> Shed 480 4 2009 WD Normal
## 101 <NA> <NA> <NA> 0 2 2010 WD Normal
## 105 <NA> <NA> <NA> 0 6 2007 WD Normal
## 112 <NA> <NA> <NA> 0 4 2010 WD Normal
## 114 <NA> MnPrv <NA> 0 10 2007 COD Abnorml
## 117 <NA> <NA> <NA> 0 9 2009 WD Normal
## 121 <NA> <NA> <NA> 0 10 2006 WD Normal
## 127 <NA> <NA> <NA> 0 2 2007 WD Normal
## 132 <NA> <NA> <NA> 0 7 2009 WD Normal
## 134 <NA> <NA> <NA> 0 6 2009 WD Normal
## 137 <NA> <NA> <NA> 0 7 2007 WD Normal
## 148 <NA> <NA> <NA> 0 5 2010 WD Normal
## 150 <NA> <NA> <NA> 0 4 2006 WD Normal
## 153 <NA> GdWo <NA> 0 6 2006 WD Normal
## 154 <NA> <NA> <NA> 0 3 2008 WD Normal
## 161 <NA> <NA> <NA> 0 6 2008 WD Normal
## 167 <NA> GdWo <NA> 0 11 2009 COD Normal
## 170 <NA> <NA> <NA> 0 1 2006 WD Normal
## 171 <NA> <NA> <NA> 0 5 2007 WD Normal
## 178 <NA> <NA> <NA> 0 7 2006 WD Normal
## 181 <NA> <NA> <NA> 0 6 2007 WD Normal
## 187 <NA> GdPrv <NA> 0 6 2009 WD Normal
## 192 <NA> <NA> <NA> 0 6 2007 WD Normal
## 204 <NA> <NA> <NA> 0 1 2008 WD Normal
## 208 <NA> GdWo <NA> 0 4 2008 WD Normal
## 209 <NA> <NA> <NA> 0 4 2007 WD Normal
## 215 <NA> MnPrv Shed 450 3 2010 WD Normal
## 219 <NA> <NA> <NA> 0 5 2008 WD Normal
## 222 <NA> <NA> <NA> 0 12 2009 ConLI Normal
## 237 <NA> <NA> <NA> 0 2 2010 WD Normal
## 244 <NA> <NA> <NA> 0 5 2010 WD Normal
## 249 <NA> <NA> Shed 500 6 2007 WD Normal
## 269 <NA> GdPrv <NA> 0 5 2007 WD Normal
## 287 <NA> <NA> <NA> 0 6 2006 WD Normal
## 288 <NA> MnPrv <NA> 0 2 2010 WD Normal
## 293 <NA> <NA> <NA> 0 3 2006 WD Normal
## 307 <NA> MnPrv <NA> 0 3 2008 WD Normal
## 308 <NA> <NA> <NA> 0 3 2009 WD Normal
## 310 <NA> <NA> <NA> 0 5 2006 WD Normal
## 319 <NA> <NA> <NA> 0 6 2009 WD Normal
## 328 <NA> <NA> <NA> 0 7 2009 WD Normal
## 330 <NA> <NA> <NA> 0 11 2007 WD Normal
## 335 <NA> <NA> Shed 700 8 2008 WD Normal
## 342 <NA> <NA> <NA> 0 5 2006 WD Normal
## 346 <NA> <NA> Gar2 15500 4 2007 WD Normal
## 347 <NA> <NA> <NA> 0 12 2009 WD Normal
## 351 <NA> <NA> <NA> 0 12 2006 WD Abnorml
## 356 <NA> <NA> <NA> 0 7 2009 WD Normal
## 360 <NA> MnPrv <NA> 0 6 2007 WD Normal
## 361 <NA> <NA> <NA> 0 7 2008 WD Normal
## 364 <NA> <NA> <NA> 0 7 2006 WD Normal
## 366 <NA> <NA> <NA> 0 7 2009 WD Normal
## 369 <NA> <NA> <NA> 0 3 2010 WD Normal
## 370 <NA> <NA> <NA> 0 1 2006 WD Normal
## 375 <NA> <NA> <NA> 0 3 2009 WD Normal
## 384 <NA> <NA> <NA> 0 6 2007 WD Normal
## 392 <NA> MnPrv Shed 1200 7 2007 WD Normal
## 393 <NA> GdWo <NA> 0 4 2006 WD Abnorml
## 404 <NA> <NA> <NA> 0 5 2007 WD Normal
## 405 <NA> GdWo <NA> 0 6 2009 WD Normal
## 412 <NA> <NA> <NA> 0 6 2010 New Partial
## 421 <NA> <NA> <NA> 0 6 2009 WD Normal
## 426 <NA> <NA> <NA> 0 8 2009 WD Normal
## 447 <NA> <NA> <NA> 0 7 2006 WD Normal
## 452 <NA> <NA> <NA> 0 7 2007 WD Normal
## 457 <NA> <NA> <NA> 0 3 2008 WD Normal
## 458 <NA> MnPrv <NA> 0 6 2008 WD Normal
## 459 <NA> <NA> <NA> 0 7 2009 WD Normal
## 465 <NA> <NA> <NA> 0 5 2006 WD Normal
## 470 <NA> <NA> <NA> 0 6 2010 WD Normal
## 484 <NA> <NA> <NA> 0 3 2007 WD Normal
## 490 <NA> <NA> <NA> 0 6 2008 WD Normal
## 496 <NA> <NA> <NA> 0 5 2007 WD Normal
## 516 <NA> GdPrv <NA> 0 8 2009 COD Abnorml
## 518 <NA> MnPrv <NA> 0 5 2007 WD Normal
## 536 <NA> MnWw <NA> 0 4 2008 COD Normal
## 537 <NA> <NA> <NA> 0 7 2006 WD Normal
## 538 <NA> MnPrv Shed 2000 5 2010 WD Normal
## 540 <NA> <NA> <NA> 0 6 2007 WD Normal
## 544 <NA> <NA> <NA> 0 2 2006 WD Normal
## 558 <NA> <NA> <NA> 0 10 2006 WD Normal
## 559 <NA> <NA> <NA> 0 5 2010 WD Normal
## 563 <NA> <NA> <NA> 0 7 2006 WD Normal
## 568 <NA> <NA> <NA> 0 12 2006 WD Normal
## 579 <NA> <NA> <NA> 0 6 2007 WD Normal
## 592 <NA> <NA> <NA> 0 5 2009 WD Normal
## 609 <NA> <NA> <NA> 0 5 2009 WD Normal
## 610 <NA> MnPrv Shed 500 7 2007 WD Normal
## 611 <NA> <NA> <NA> 0 11 2009 WD Normal
## 615 <NA> <NA> <NA> 0 6 2006 WD Normal
## 622 <NA> <NA> <NA> 0 6 2007 WD Normal
## 625 <NA> GdWo Shed 600 8 2007 WD Normal
## 640 <NA> <NA> <NA> 0 5 2007 WD Normal
## 644 <NA> <NA> <NA> 0 3 2007 WD Normal
## 658 <NA> <NA> <NA> 0 11 2007 WD Normal
## 664 <NA> <NA> <NA> 0 8 2007 WD Abnorml
## 666 <NA> <NA> <NA> 0 11 2006 WD Normal
## 670 <NA> <NA> <NA> 0 6 2006 WD Normal
## 677 <NA> <NA> <NA> 0 10 2007 WD Normal
## 680 <NA> <NA> <NA> 0 11 2008 WD Normal
## 683 <NA> <NA> <NA> 0 9 2007 WD Normal
## 685 <NA> <NA> <NA> 0 3 2007 WD Normal
## 688 <NA> <NA> <NA> 0 5 2008 WD Normal
## 704 <NA> <NA> <NA> 0 6 2007 WD Normal
## 707 <NA> MnPrv <NA> 0 12 2008 WD Abnorml
## 712 <NA> <NA> <NA> 0 3 2010 WD Normal
## 718 <NA> <NA> <NA> 0 12 2006 WD Normal
## 719 <NA> <NA> <NA> 0 5 2010 WD Normal
## 724 <NA> <NA> <NA> 0 12 2009 WD Normal
## 732 <NA> <NA> <NA> 0 5 2007 WD Family
## 743 <NA> <NA> <NA> 0 7 2008 WD Normal
## 744 <NA> <NA> <NA> 0 4 2009 WD Normal
## 749 <NA> <NA> <NA> 0 8 2007 WD Normal
## 755 <NA> GdPrv <NA> 0 4 2010 WD Abnorml
## 768 <NA> <NA> <NA> 0 4 2009 WD Normal
## 781 <NA> <NA> <NA> 0 7 2009 WD Normal
## 783 <NA> <NA> <NA> 0 9 2009 WD Normal
## 787 <NA> <NA> <NA> 0 7 2007 WD Normal
## 789 <NA> <NA> <NA> 0 5 2007 WD Normal
## 792 <NA> <NA> Shed 500 10 2008 WD Normal
## 809 <NA> <NA> <NA> 0 6 2008 ConLD Normal
## 814 <NA> <NA> <NA> 0 7 2006 WD Normal
## 815 <NA> <NA> <NA> 0 7 2008 WD Normal
## 820 <NA> <NA> <NA> 0 10 2007 WD Family
## 826 <NA> <NA> <NA> 0 6 2009 WD Abnorml
## 838 <NA> <NA> <NA> 0 3 2008 WD Normal
## 843 <NA> <NA> <NA> 0 1 2007 WD Normal
## 849 <NA> <NA> <NA> 0 5 2006 WD Normal
## 851 <NA> MnPrv <NA> 0 8 2009 WD Normal
## 853 <NA> <NA> <NA> 0 4 2010 WD Normal
## 854 <NA> MnPrv <NA> 0 10 2008 WD Normal
## 857 <NA> <NA> <NA> 0 8 2006 WD Normal
## 863 <NA> MnPrv <NA> 0 8 2009 WD Normal
## 866 <NA> <NA> <NA> 0 5 2006 WD Normal
## 877 <NA> GdWo <NA> 0 7 2009 WD Normal
## 880 <NA> MnPrv <NA> 0 12 2009 WD Normal
## 891 <NA> GdPrv <NA> 0 6 2008 WD Normal
## 898 <NA> <NA> <NA> 0 6 2007 WD Normal
## 902 <NA> MnPrv <NA> 0 8 2007 WD Normal
## 906 <NA> MnPrv <NA> 0 6 2006 WD Normal
## 909 <NA> <NA> <NA> 0 10 2009 WD Normal
## 915 <NA> <NA> <NA> 0 4 2009 WD Normal
## 923 <NA> <NA> <NA> 0 3 2008 WD Abnorml
## 925 <NA> GdPrv <NA> 0 4 2008 WD Normal
## 926 <NA> <NA> <NA> 0 6 2009 WD Normal
## 927 <NA> <NA> <NA> 0 11 2006 WD Normal
## 936 <NA> <NA> <NA> 0 6 2010 COD Normal
## 938 <NA> GdPrv <NA> 0 6 2009 WD Normal
## 941 <NA> <NA> <NA> 0 1 2009 COD Abnorml
## 950 <NA> MnPrv Shed 400 9 2008 WD Normal
## 958 <NA> <NA> <NA> 0 7 2008 WD Normal
## 964 <NA> <NA> <NA> 0 7 2008 WD Normal
## 971 <NA> <NA> <NA> 0 4 2006 WD Normal
## 975 <NA> <NA> <NA> 0 7 2008 WD Normal
## 978 <NA> <NA> <NA> 0 5 2008 WD Normal
## 983 <NA> <NA> <NA> 0 6 2007 WD Normal
## 991 <NA> <NA> <NA> 0 1 2006 COD Normal
## 992 <NA> <NA> <NA> 0 2 2009 WD Normal
## 998 <NA> <NA> <NA> 0 6 2007 WD Normal
## 1001 <NA> <NA> <NA> 0 3 2007 WD Normal
## 1012 <NA> <NA> <NA> 0 8 2009 COD Abnorml
## 1013 <NA> <NA> <NA> 0 5 2007 WD Normal
## 1019 <NA> <NA> <NA> 0 5 2008 COD Abnorml
## 1025 <NA> <NA> <NA> 0 7 2006 WD Normal
## 1027 <NA> <NA> <NA> 0 11 2006 WD Abnorml
## 1028 <NA> <NA> <NA> 0 2 2006 WD Normal
## 1030 <NA> <NA> <NA> 0 1 2009 WD Normal
## 1032 <NA> <NA> <NA> 0 9 2008 WD Normal
## 1036 <NA> <NA> <NA> 0 7 2008 WD Normal
## 1040 <NA> <NA> <NA> 0 6 2009 WD Normal
## 1052 <NA> <NA> <NA> 0 1 2009 WD Normal
## 1054 <NA> <NA> <NA> 0 3 2007 WD Normal
## 1059 <NA> MnPrv <NA> 0 11 2009 WD Normal
## 1072 <NA> <NA> <NA> 0 3 2006 WD Abnorml
## 1079 <NA> <NA> <NA> 0 7 2006 WD Normal
## 1081 <NA> <NA> <NA> 0 5 2010 WD Normal
## 1092 <NA> <NA> <NA> 0 10 2007 WD Normal
## 1103 <NA> <NA> <NA> 0 11 2007 WD Abnorml
## 1105 <NA> <NA> <NA> 0 6 2008 WD Normal
## 1111 <NA> <NA> <NA> 0 3 2009 WD Normal
## 1117 <NA> MnPrv <NA> 0 10 2009 COD Abnorml
## 1119 <NA> <NA> <NA> 0 7 2007 WD Normal
## 1133 <NA> <NA> <NA> 0 5 2009 WD Normal
## 1136 <NA> <NA> <NA> 0 10 2009 WD Normal
## 1138 <NA> <NA> <NA> 0 7 2008 WD Normal
## 1141 <NA> <NA> <NA> 0 5 2006 WD Normal
## 1143 <NA> <NA> <NA> 0 8 2008 WD Normal
## 1148 <NA> <NA> <NA> 0 6 2008 WD Normal
## 1149 <NA> GdPrv <NA> 0 5 2008 WD Normal
## 1156 <NA> <NA> <NA> 0 11 2008 WD Normal
## 1159 <NA> <NA> <NA> 0 6 2007 WD Normal
## 1172 <NA> <NA> <NA> 0 12 2009 WD Normal
## 1175 <NA> GdPrv <NA> 0 4 2006 WD Normal
## 1185 <NA> <NA> <NA> 0 3 2007 WD Normal
## 1188 <NA> <NA> <NA> 0 6 2009 WD Normal
## 1201 <NA> MnPrv <NA> 0 11 2006 WD Normal
## 1208 <NA> <NA> <NA> 0 5 2006 WD Normal
## 1225 <NA> <NA> Gar2 8300 8 2007 WD Normal
## 1228 <NA> MnPrv <NA> 0 5 2010 COD Abnorml
## 1238 <NA> <NA> <NA> 0 6 2006 WD Normal
## 1241 <NA> <NA> <NA> 0 5 2010 WD Normal
## 1245 <NA> <NA> <NA> 0 3 2006 WD Normal
## 1247 <NA> MnPrv <NA> 0 7 2007 WD Normal
## 1254 <NA> <NA> <NA> 0 6 2009 WD Normal
## 1256 <NA> <NA> <NA> 0 11 2009 WD Normal
## 1262 <NA> GdWo <NA> 0 5 2008 WD Normal
## 1264 <NA> <NA> <NA> 0 4 2010 WD Normal
## 1265 <NA> <NA> <NA> 0 8 2009 WD Normal
## 1266 <NA> MnPrv <NA> 0 11 2006 WD Normal
## 1270 <NA> <NA> <NA> 0 12 2009 WD Normal
## 1271 <NA> <NA> <NA> 0 6 2009 WD Normal
## 1279 <NA> <NA> <NA> 0 6 2010 WD Normal
## 1280 <NA> <NA> <NA> 0 6 2006 WD Normal
## 1283 <NA> <NA> <NA> 0 4 2010 WD Normal
## 1293 <NA> <NA> <NA> 0 5 2009 WD Normal
## 1294 <NA> <NA> <NA> 0 11 2009 WD Normal
## 1302 <NA> GdWo <NA> 0 6 2008 WD Normal
## 1305 <NA> <NA> <NA> 0 6 2007 WD Normal
## 1311 <NA> <NA> <NA> 0 8 2006 WD Normal
## 1314 <NA> <NA> <NA> 0 7 2008 WD Normal
## 1335 <NA> <NA> <NA> 0 8 2007 WD Normal
## 1339 <NA> <NA> <NA> 0 6 2006 WD Normal
## 1341 <NA> <NA> <NA> 0 8 2007 WD Normal
## 1347 <NA> <NA> <NA> 0 6 2008 WD Normal
## 1349 <NA> GdWo <NA> 0 10 2008 WD Normal
## 1350 <NA> <NA> <NA> 0 4 2010 WD Normal
## 1351 <NA> <NA> <NA> 0 6 2010 WD Normal
## 1355 <NA> <NA> <NA> 0 6 2009 WD Normal
## 1358 <NA> <NA> <NA> 0 1 2010 WD Normal
## 1361 <NA> <NA> <NA> 0 6 2009 WD Normal
## 1366 <NA> <NA> <NA> 0 3 2007 WD Normal
## 1373 <NA> <NA> <NA> 0 5 2008 WD Normal
## 1375 <NA> <NA> <NA> 0 8 2007 WD Normal
## 1388 <NA> <NA> <NA> 0 6 2010 WD Normal
## 1399 <NA> MnPrv <NA> 0 3 2009 WD Normal
## 1409 <NA> <NA> <NA> 0 5 2009 WD Normal
## 1411 <NA> <NA> <NA> 0 12 2006 WD Normal
## 1415 Gd GdPrv <NA> 0 8 2006 WD Alloca
## 1416 <NA> GdWo <NA> 0 6 2007 WD Normal
## 1421 <NA> <NA> <NA> 0 4 2007 WD Normal
## 1423 <NA> <NA> <NA> 0 10 2009 WD Normal
## 1433 <NA> <NA> <NA> 0 5 2008 WD Normal
## 1435 <NA> <NA> <NA> 0 5 2009 WD Normal
## 1438 <NA> <NA> <NA> 0 4 2010 WD Normal
## SalePrice
## 8 200000
## 13 144000
## 15 157000
## 17 149000
## 25 154000
## 32 149350
## 43 144000
## 44 130250
## 51 177000
## 65 219500
## 67 180000
## 77 135750
## 85 168500
## 96 185000
## 101 205000
## 105 169500
## 112 180000
## 114 217000
## 117 139000
## 121 180000
## 127 128000
## 132 244000
## 134 220000
## 137 143000
## 148 222500
## 150 115000
## 153 190000
## 154 235000
## 161 162500
## 167 190000
## 170 228000
## 171 128500
## 178 172500
## 181 177000
## 187 173000
## 192 184000
## 204 149000
## 208 141000
## 209 277000
## 215 161750
## 219 311500
## 222 200000
## 237 194500
## 244 205000
## 249 277000
## 269 148000
## 287 88000
## 288 122000
## 293 235000
## 307 89500
## 308 82500
## 310 165600
## 319 187500
## 328 214500
## 330 119000
## 335 228950
## 342 87500
## 346 151500
## 347 157500
## 351 190000
## 356 173000
## 360 156000
## 361 145000
## 364 190000
## 366 159000
## 369 162000
## 370 172400
## 375 61000
## 384 240000
## 392 106500
## 393 100000
## 404 168000
## 405 150000
## 412 222000
## 421 215000
## 426 275000
## 447 199900
## 452 204000
## 457 256000
## 458 161000
## 459 110000
## 465 178740
## 470 212000
## 484 132500
## 490 115000
## 496 430000
## 516 158000
## 518 211000
## 536 111250
## 537 158000
## 538 272000
## 540 248000
## 544 229000
## 558 234000
## 559 121500
## 563 268000
## 568 135960
## 579 181900
## 592 140000
## 609 313000
## 610 148000
## 611 261500
## 615 183200
## 622 168500
## 625 139900
## 640 226000
## 644 143250
## 658 197900
## 664 129000
## 666 168000
## 670 165000
## 677 128500
## 680 173000
## 683 207500
## 685 148800
## 688 141000
## 704 302000
## 707 109900
## 712 130500
## 718 275000
## 719 143000
## 724 222000
## 732 108000
## 743 299800
## 744 236000
## 749 162000
## 755 158900
## 768 134900
## 781 165500
## 783 161500
## 787 187500
## 789 146800
## 792 194500
## 809 144500
## 814 137000
## 815 271000
## 820 225000
## 826 185000
## 838 140000
## 843 171000
## 849 215000
## 851 158000
## 853 127000
## 854 147000
## 857 250000
## 863 148500
## 866 169000
## 877 136500
## 880 178000
## 891 165000
## 898 110000
## 902 125500
## 906 131000
## 909 143500
## 915 135000
## 923 175000
## 925 176000
## 926 236500
## 927 222000
## 936 244400
## 938 214000
## 941 137500
## 950 172000
## 958 272000
## 964 135000
## 971 165000
## 975 178400
## 978 255900
## 983 195000
## 991 136500
## 992 185000
## 998 136905
## 1001 163500
## 1012 187500
## 1013 160000
## 1019 287000
## 1025 160000
## 1027 310000
## 1028 230000
## 1030 84000
## 1032 287000
## 1036 173000
## 1040 139600
## 1052 248000
## 1054 220000
## 1059 154000
## 1072 138800
## 1079 187500
## 1081 83500
## 1092 170000
## 1103 181000
## 1105 188000
## 1111 184100
## 1117 112000
## 1119 163900
## 1133 196000
## 1136 197500
## 1138 80000
## 1141 180000
## 1143 116900
## 1148 120500
## 1149 201800
## 1156 224000
## 1159 194000
## 1172 115000
## 1175 250000
## 1185 168000
## 1188 165000
## 1201 107000
## 1208 145000
## 1225 190000
## 1228 142000
## 1238 230000
## 1241 169900
## 1245 171750
## 1247 294000
## 1254 181000
## 1256 161500
## 1262 381000
## 1264 260000
## 1265 185750
## 1266 137000
## 1270 162000
## 1271 197900
## 1279 143000
## 1280 190000
## 1283 180500
## 1293 225000
## 1294 177500
## 1302 179200
## 1305 302000
## 1311 275000
## 1314 72500
## 1335 228500
## 1339 262500
## 1341 215000
## 1347 235000
## 1349 110000
## 1350 149900
## 1351 177500
## 1355 104900
## 1358 216000
## 1361 144000
## 1366 466500
## 1373 237500
## 1375 112000
## 1388 160000
## 1399 112000
## 1409 340000
## 1411 223000
## 1415 274970
## 1416 144000
## 1421 182900
## 1423 143750
## 1433 149300
## 1435 121000
## 1438 157900A total of 257 rows contain NA values, this constitutes 17.7% of the data, after dropping 9 rows from NA’s in Elctrical & MasVnrType
Data Exploration
8.
hist(housing$SalePrice)
Sales Price is right-skewed, so the mean is greater than the median.
9.
plot(SalePrice~., data=housing)
Sales Price appears to have correlation with: MSZoning (specifically Residential Low Density seems to correlate with higher prices), Street (Paved = higher prices), Alley (homes with no Alley or paved Alley have higher prices), Neighborhood (specific neighborhoods correlate with higher prices), Condition2 (Adjacent or near off-site features correlate with higher prices), BldgType, OverallQual, OverallCond, YearBuilt, YearRemodAdd (may be a better variable than YearBuilt, since remodel age = year built if no remodel has occured), RoofMatl, ExterQual, BsmtQual, BsmtExposure, BsmtFinType1, BstFinSF1, TotalBsmtSF, HeatingQC, CentralAir, Electrical, 1stFlrSF, 2ndFlrSF, GrLivArea, FullBath, KitchenQual, TotRmsAbvGrd, FireplaceQu, GarageType, GarageFinish, GarageCars, GarageArea, GarageQual, GarageCond, PavedDrive, PoolQC, SaleType, SaleCondition
10.
library(caret)## Loading required package: ggplot2## Loading required package: latticeset.seed(123)
intrain <- createDataPartition(housing$SalePrice, p = .80, list = FALSE)
housing.train <- housing[intrain, ]
housing.test <- housing[-intrain, ]Creating Predictive Models
11.
library(glmnet)## Loading required package: Matrix##
## Attaching package: 'Matrix'## The following objects are masked from 'package:tidyr':
##
## expand, pack, unpack## Loaded glmnet 4.1-4set.seed(1)
lasso <- train(SalePrice ~ ., data= housing.train, preProc = "knnImpute", na.action = na.pass, method = "glmnet", trControl = trainControl("cv", number = 10), tuneGrid = expand.grid(alpha = 1, lambda = 10^seq(-3,3, length = 100)))## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stCBlock, Exterior1stImStucc, Exterior2ndCBlock,
## ElectricalMix, MiscFeatureTenC## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlMembran,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc, Exterior1stStone## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition1RRNe, Condition2PosN,
## Condition2RRNn, RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAe, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, HeatingQCPo## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAn, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: UtilitiesNoSeWa, Condition2PosA,
## Condition2RRNn, RoofMatlMetal, RoofMatlRoll, Exterior1stAsphShn,
## Exterior1stImStucc## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, Exterior2ndOther, ExterCondPo,
## SaleTypeCon## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, FunctionalSev## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucccoef(lasso$finalModel, lasso$bestTune$lambda)## 275 x 1 sparse Matrix of class "dgCMatrix"
## s1
## (Intercept) 1.808748e+05
## MSSubClass -4.855969e+03
## MSZoningFV .
## MSZoningRH .
## MSZoningRL 1.901305e+03
## MSZoningRM .
## LotFrontage .
## LotArea 9.135331e+02
## StreetPave 1.203417e+03
## AlleyPave .
## AlleyNA .
## LotShapeIR2 1.293812e+03
## LotShapeIR3 -2.428685e+03
## LotShapeReg .
## LandContourHLS .
## LandContourLow .
## LandContourLvl 1.693954e+02
## UtilitiesNoSeWa -6.794625e+02
## LotConfigCulDSac 2.712483e+03
## LotConfigFR2 .
## LotConfigFR3 -1.943216e+02
## LotConfigInside .
## LandSlopeMod 3.453039e+01
## LandSlopeSev -3.111790e+02
## NeighborhoodBlueste .
## NeighborhoodBrDale .
## NeighborhoodBrkSide 1.035020e+03
## NeighborhoodClearCr 1.002119e+03
## NeighborhoodCollgCr .
## NeighborhoodCrawfor 4.474473e+03
## NeighborhoodEdwards -1.468655e+03
## NeighborhoodGilbert .
## NeighborhoodIDOTRR .
## NeighborhoodMeadowV -2.696641e+02
## NeighborhoodMitchel .
## NeighborhoodNAmes .
## NeighborhoodNoRidge 5.081346e+03
## NeighborhoodNPkVill .
## NeighborhoodNridgHt 5.670871e+03
## NeighborhoodNWAmes -6.888416e+02
## NeighborhoodOldTown -5.825281e+01
## NeighborhoodSawyer .
## NeighborhoodSawyerW 9.118547e+02
## NeighborhoodSomerst 2.970225e+03
## NeighborhoodStoneBr 5.203892e+03
## NeighborhoodSWISU -8.703419e+01
## NeighborhoodTimber .
## NeighborhoodVeenker 1.012276e+02
## Condition1Feedr -4.186221e+02
## Condition1Norm 2.991711e+03
## Condition1PosA .
## Condition1PosN .
## Condition1RRAe -7.485716e+02
## Condition1RRAn 4.974039e+02
## Condition1RRNe .
## Condition1RRNn .
## Condition2Feedr .
## Condition2Norm .
## Condition2PosA 1.434151e+02
## Condition2PosN -9.870577e+03
## Condition2RRAe -1.367026e+00
## Condition2RRAn .
## Condition2RRNn .
## BldgType2fmCon 1.949780e+02
## BldgTypeDuplex -1.433948e+03
## BldgTypeTwnhs -1.125001e+03
## BldgTypeTwnhsE -9.260561e+02
## HouseStyle1.5Unf 3.735028e+02
## HouseStyle1Story .
## HouseStyle2.5Fin -7.490851e+02
## HouseStyle2.5Unf .
## HouseStyle2Story .
## HouseStyleSFoyer .
## HouseStyleSLvl 2.952193e+02
## OverallQual2 .
## OverallQual3 -5.463212e+02
## OverallQual4 -7.887650e+02
## OverallQual5 -6.503855e+02
## OverallQual6 .
## OverallQual7 2.123743e+03
## OverallQual8 8.359035e+03
## OverallQual9 1.256765e+04
## OverallQual10 6.564475e+03
## OverallCond2 -1.111678e+02
## OverallCond3 -2.649090e+03
## OverallCond4 -1.394846e+03
## OverallCond5 -2.711153e+03
## OverallCond6 .
## OverallCond7 1.667515e+03
## OverallCond8 7.328050e+02
## OverallCond9 1.116484e+03
## YearBuilt 6.972904e+03
## YearRemodAdd 2.300398e+03
## RoofStyleGable -1.923172e+01
## RoofStyleGambrel .
## RoofStyleHip .
## RoofStyleMansard 2.641756e+02
## RoofStyleShed 1.730142e+02
## RoofMatlCompShg 1.891926e+03
## RoofMatlMembran 8.361410e+02
## RoofMatlMetal .
## RoofMatlRoll .
## RoofMatlTar&Grv 8.137608e+01
## RoofMatlWdShake 4.877634e+02
## RoofMatlWdShngl 6.634192e+03
## Exterior1stAsphShn .
## Exterior1stBrkComm .
## Exterior1stBrkFace 2.624487e+03
## Exterior1stCBlock .
## Exterior1stCemntBd 1.638894e+03
## Exterior1stHdBoard -2.902244e+02
## Exterior1stImStucc .
## Exterior1stMetalSd .
## Exterior1stPlywood 1.449538e+02
## Exterior1stStone .
## Exterior1stStucco .
## Exterior1stVinylSd .
## Exterior1stWd Sdng .
## Exterior1stWdShing -2.258862e+02
## Exterior2ndAsphShn .
## Exterior2ndBrk Cmn .
## Exterior2ndBrkFace .
## Exterior2ndCBlock .
## Exterior2ndCmentBd .
## Exterior2ndHdBoard .
## Exterior2ndImStucc .
## Exterior2ndMetalSd .
## Exterior2ndOther -3.133446e+02
## Exterior2ndPlywood .
## Exterior2ndStone .
## Exterior2ndStucco -9.748898e+02
## Exterior2ndVinylSd 8.582581e+02
## Exterior2ndWd Sdng .
## Exterior2ndWd Shng -2.570788e+02
## MasVnrTypeBrkFace -6.407364e+02
## MasVnrTypeNone .
## MasVnrTypeStone 2.896943e+02
## MasVnrArea 2.756441e+03
## ExterQualFa -1.165190e+02
## ExterQualGd .
## ExterQualTA -2.068488e+03
## ExterCondFa .
## ExterCondGd .
## ExterCondPo -3.420444e+02
## ExterCondTA 3.438523e+02
## FoundationCBlock .
## FoundationPConc .
## FoundationSlab -2.966875e+02
## FoundationStone 9.658727e+01
## FoundationWood -5.376220e+02
## BsmtQualFa .
## BsmtQualGd -2.699710e+03
## BsmtQualTA -1.559807e+03
## BsmtQualNA -4.356053e+02
## BsmtCondGd .
## BsmtCondPo -5.093603e+02
## BsmtCondTA 8.162638e+02
## BsmtCondNA -6.576593e+00
## BsmtExposureGd 5.699746e+03
## BsmtExposureMn .
## BsmtExposureNo -2.342891e+03
## BsmtExposureNA -1.249622e+03
## BsmtFinType1BLQ .
## BsmtFinType1GLQ 2.460948e+03
## BsmtFinType1LwQ .
## BsmtFinType1Rec .
## BsmtFinType1Unf -1.335871e+03
## BsmtFinType1NA -2.801005e+02
## BsmtFinSF1 2.520294e+03
## BsmtFinType2BLQ .
## BsmtFinType2GLQ 5.634077e+02
## BsmtFinType2LwQ .
## BsmtFinType2Rec -2.006174e+02
## BsmtFinType2Unf .
## BsmtFinType2NA .
## BsmtFinSF2 .
## BsmtUnfSF .
## TotalBsmtSF 1.388839e+03
## HeatingGasA .
## HeatingGasW .
## HeatingGrav .
## HeatingOthW -9.819097e+02
## HeatingWall .
## HeatingQCFa .
## HeatingQCGd -4.288629e+02
## HeatingQCPo .
## HeatingQCTA -8.634036e+02
## CentralAirY 1.085297e+02
## ElectricalFuseF .
## ElectricalFuseP .
## ElectricalMix .
## ElectricalSBrkr .
## X1stFlrSF 9.851853e+02
## X2ndFlrSF .
## LowQualFinSF -7.377839e+02
## GrLivArea 2.569158e+04
## BsmtFullBath 2.920552e+03
## BsmtHalfBath 4.738209e+01
## FullBath 4.039362e+03
## HalfBath 1.094228e+01
## BedroomAbvGr -5.372127e+02
## KitchenAbvGr -2.773361e+03
## KitchenQualFa -1.137243e+03
## KitchenQualGd -3.928501e+03
## KitchenQualTA -4.775088e+03
## TotRmsAbvGrd 2.099617e+03
## FunctionalMaj2 -8.746126e+01
## FunctionalMin1 -3.870090e+00
## FunctionalMin2 .
## FunctionalMod .
## FunctionalSev -9.239911e+02
## FunctionalTyp 2.876395e+03
## Fireplaces 8.452490e+02
## FireplaceQuFa .
## FireplaceQuGd 1.190241e+02
## FireplaceQuPo .
## FireplaceQuTA -4.473275e+02
## FireplaceQuNA -2.581906e+03
## GarageTypeAttchd .
## GarageTypeBasment -6.211299e+02
## GarageTypeBuiltIn 1.374027e+03
## GarageTypeCarPort .
## GarageTypeDetchd .
## GarageTypeNA .
## GarageYrBlt .
## GarageFinishRFn .
## GarageFinishUnf .
## GarageFinishNA .
## GarageCars 6.930239e+03
## GarageArea 1.593557e+01
## GarageQualFa -7.738352e+02
## GarageQualGd 7.220481e+02
## GarageQualPo .
## GarageQualTA .
## GarageQualNA .
## GarageCondFa -2.990867e+02
## GarageCondGd .
## GarageCondPo .
## GarageCondTA .
## GarageCondNA .
## PavedDriveP .
## PavedDriveY 5.107758e+02
## WoodDeckSF 1.605118e+03
## OpenPorchSF 1.106775e+03
## EnclosedPorch 3.298012e+02
## X3SsnPorch 5.028829e+02
## ScreenPorch 2.176282e+03
## PoolArea 1.739860e+04
## PoolQCFa -9.467740e+03
## PoolQCGd -1.705031e+04
## PoolQCNA .
## FenceGdWo -5.559720e+02
## FenceMnPrv -1.517562e+02
## FenceMnWw -5.781862e+00
## FenceNA .
## MiscFeatureOthr .
## MiscFeatureShed .
## MiscFeatureTenC .
## MiscFeatureNA .
## MiscVal .
## MoSold -7.637603e+02
## YrSold .
## SaleTypeCon 9.338769e+02
## SaleTypeConLD .
## SaleTypeConLI .
## SaleTypeConLw .
## SaleTypeCWD 6.399739e-01
## SaleTypeNew 7.686916e+03
## SaleTypeOth .
## SaleTypeWD .
## SaleConditionAdjLand 3.888843e+02
## SaleConditionAlloca 6.327893e+02
## SaleConditionFamily -4.075413e+01
## SaleConditionNormal 1.995598e+03
## SaleConditionPartial .predictions.lasso <- predict(lasso, housing.test, na.action = na.pass)
RMSE(predictions.lasso, housing.test$SalePrice)## [1] 34113.53Several variable coefficients were shrunk to zero, meaning that they were not used for this prediction model. RMSE = 34113.53
12.
set.seed(1)
ridge <- train(SalePrice ~ ., data= housing.train, preProc = "knnImpute", na.action = na.pass, method = "glmnet", trControl = trainControl("cv", number = 10), tuneGrid = expand.grid(alpha = 0, lambda = 10^seq(-3,3, length = 100)))## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stCBlock, Exterior1stImStucc, Exterior2ndCBlock,
## ElectricalMix, MiscFeatureTenC## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlMembran,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc, Exterior1stStone## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition1RRNe, Condition2PosN,
## Condition2RRNn, RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAe, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, HeatingQCPo## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAn, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: UtilitiesNoSeWa, Condition2PosA,
## Condition2RRNn, RoofMatlMetal, RoofMatlRoll, Exterior1stAsphShn,
## Exterior1stImStucc## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, Exterior2ndOther, ExterCondPo,
## SaleTypeCon## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, FunctionalSev## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStuccpredictions.ridge <- predict(ridge, housing.test, na.action = na.pass)
RMSE(predictions.ridge, housing.test$SalePrice)## [1] 32406.35RMSE = 32406.35
13.
set.seed(1)
enet <- train(SalePrice ~ ., data= housing.train, preProc = "knnImpute", na.action = na.pass, method = "glmnet", trControl = trainControl("cv", number = 10), tuneGrid = expand.grid(alpha = seq(0, 1, length = 10), lambda = 10^seq(-3,3, length = 100)))## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stCBlock, Exterior1stImStucc, Exterior2ndCBlock,
## ElectricalMix, MiscFeatureTenC
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stCBlock, Exterior1stImStucc, Exterior2ndCBlock,
## ElectricalMix, MiscFeatureTenC
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stCBlock, Exterior1stImStucc, Exterior2ndCBlock,
## ElectricalMix, MiscFeatureTenC
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stCBlock, Exterior1stImStucc, Exterior2ndCBlock,
## ElectricalMix, MiscFeatureTenC
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stCBlock, Exterior1stImStucc, Exterior2ndCBlock,
## ElectricalMix, MiscFeatureTenC
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stCBlock, Exterior1stImStucc, Exterior2ndCBlock,
## ElectricalMix, MiscFeatureTenC
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stCBlock, Exterior1stImStucc, Exterior2ndCBlock,
## ElectricalMix, MiscFeatureTenC
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stCBlock, Exterior1stImStucc, Exterior2ndCBlock,
## ElectricalMix, MiscFeatureTenC
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stCBlock, Exterior1stImStucc, Exterior2ndCBlock,
## ElectricalMix, MiscFeatureTenC
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stCBlock, Exterior1stImStucc, Exterior2ndCBlock,
## ElectricalMix, MiscFeatureTenC## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlMembran,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc, Exterior1stStone
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlMembran,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc, Exterior1stStone
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlMembran,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc, Exterior1stStone
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlMembran,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc, Exterior1stStone
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlMembran,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc, Exterior1stStone
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlMembran,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc, Exterior1stStone
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlMembran,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc, Exterior1stStone
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlMembran,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc, Exterior1stStone
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlMembran,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc, Exterior1stStone
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlMembran,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc, Exterior1stStone## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition1RRNe, Condition2PosN,
## Condition2RRNn, RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition1RRNe, Condition2PosN,
## Condition2RRNn, RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition1RRNe, Condition2PosN,
## Condition2RRNn, RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition1RRNe, Condition2PosN,
## Condition2RRNn, RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition1RRNe, Condition2PosN,
## Condition2RRNn, RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition1RRNe, Condition2PosN,
## Condition2RRNn, RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition1RRNe, Condition2PosN,
## Condition2RRNn, RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition1RRNe, Condition2PosN,
## Condition2RRNn, RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition1RRNe, Condition2PosN,
## Condition2RRNn, RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition1RRNe, Condition2PosN,
## Condition2RRNn, RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAe, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAe, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAe, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAe, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAe, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAe, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAe, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAe, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAe, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAe, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, HeatingQCPo
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, HeatingQCPo
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, HeatingQCPo
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, HeatingQCPo
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, HeatingQCPo
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, HeatingQCPo
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, HeatingQCPo
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, HeatingQCPo
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, HeatingQCPo
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, HeatingQCPo## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAn, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAn, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAn, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAn, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAn, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAn, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAn, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAn, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAn, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAn, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: UtilitiesNoSeWa, Condition2PosA,
## Condition2RRNn, RoofMatlMetal, RoofMatlRoll, Exterior1stAsphShn,
## Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: UtilitiesNoSeWa, Condition2PosA,
## Condition2RRNn, RoofMatlMetal, RoofMatlRoll, Exterior1stAsphShn,
## Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: UtilitiesNoSeWa, Condition2PosA,
## Condition2RRNn, RoofMatlMetal, RoofMatlRoll, Exterior1stAsphShn,
## Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: UtilitiesNoSeWa, Condition2PosA,
## Condition2RRNn, RoofMatlMetal, RoofMatlRoll, Exterior1stAsphShn,
## Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: UtilitiesNoSeWa, Condition2PosA,
## Condition2RRNn, RoofMatlMetal, RoofMatlRoll, Exterior1stAsphShn,
## Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: UtilitiesNoSeWa, Condition2PosA,
## Condition2RRNn, RoofMatlMetal, RoofMatlRoll, Exterior1stAsphShn,
## Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: UtilitiesNoSeWa, Condition2PosA,
## Condition2RRNn, RoofMatlMetal, RoofMatlRoll, Exterior1stAsphShn,
## Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: UtilitiesNoSeWa, Condition2PosA,
## Condition2RRNn, RoofMatlMetal, RoofMatlRoll, Exterior1stAsphShn,
## Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: UtilitiesNoSeWa, Condition2PosA,
## Condition2RRNn, RoofMatlMetal, RoofMatlRoll, Exterior1stAsphShn,
## Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: UtilitiesNoSeWa, Condition2PosA,
## Condition2RRNn, RoofMatlMetal, RoofMatlRoll, Exterior1stAsphShn,
## Exterior1stImStucc## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, Exterior2ndOther, ExterCondPo,
## SaleTypeCon
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, Exterior2ndOther, ExterCondPo,
## SaleTypeCon
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, Exterior2ndOther, ExterCondPo,
## SaleTypeCon
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, Exterior2ndOther, ExterCondPo,
## SaleTypeCon
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, Exterior2ndOther, ExterCondPo,
## SaleTypeCon
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, Exterior2ndOther, ExterCondPo,
## SaleTypeCon
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, Exterior2ndOther, ExterCondPo,
## SaleTypeCon
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, Exterior2ndOther, ExterCondPo,
## SaleTypeCon
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, Exterior2ndOther, ExterCondPo,
## SaleTypeCon
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, Exterior2ndOther, ExterCondPo,
## SaleTypeCon## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, FunctionalSev
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, FunctionalSev
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, FunctionalSev
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, FunctionalSev
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, FunctionalSev
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, FunctionalSev
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, FunctionalSev
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, FunctionalSev
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, FunctionalSev
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, FunctionalSev## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStuccpredictions.enet <- predict(enet, housing.test, na.action = na.pass)
RMSE(predictions.enet, housing.test$SalePrice)## [1] 32406.35RMSE = 32406.35; which is identical to ridge; which indicates that an alpha of 1 was the best alpha used and optimal lamda was the same for ridge as enet.
14.
set.seed(1)
rf <- train(SalePrice ~ ., data= housing.train, preProc = "knnImpute", na.action = na.pass, importance = T, method = "rf", metric = "RMSE", trControl = trainControl("cv", number = 10), tuneGrid = expand.grid(mtry = c(5, 15, 30, 60, 79)))## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stCBlock, Exterior1stImStucc, Exterior2ndCBlock,
## ElectricalMix, MiscFeatureTenC
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stCBlock, Exterior1stImStucc, Exterior2ndCBlock,
## ElectricalMix, MiscFeatureTenC
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stCBlock, Exterior1stImStucc, Exterior2ndCBlock,
## ElectricalMix, MiscFeatureTenC
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stCBlock, Exterior1stImStucc, Exterior2ndCBlock,
## ElectricalMix, MiscFeatureTenC
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stCBlock, Exterior1stImStucc, Exterior2ndCBlock,
## ElectricalMix, MiscFeatureTenC## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlMembran,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc, Exterior1stStone
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlMembran,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc, Exterior1stStone
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlMembran,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc, Exterior1stStone
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlMembran,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc, Exterior1stStone
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlMembran,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc, Exterior1stStone## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition1RRNe, Condition2PosN,
## Condition2RRNn, RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition1RRNe, Condition2PosN,
## Condition2RRNn, RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition1RRNe, Condition2PosN,
## Condition2RRNn, RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition1RRNe, Condition2PosN,
## Condition2RRNn, RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition1RRNe, Condition2PosN,
## Condition2RRNn, RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAe, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAe, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAe, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAe, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAe, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, HeatingQCPo
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, HeatingQCPo
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, HeatingQCPo
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, HeatingQCPo
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, HeatingQCPo## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAn, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAn, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAn, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAn, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAn, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: UtilitiesNoSeWa, Condition2PosA,
## Condition2RRNn, RoofMatlMetal, RoofMatlRoll, Exterior1stAsphShn,
## Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: UtilitiesNoSeWa, Condition2PosA,
## Condition2RRNn, RoofMatlMetal, RoofMatlRoll, Exterior1stAsphShn,
## Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: UtilitiesNoSeWa, Condition2PosA,
## Condition2RRNn, RoofMatlMetal, RoofMatlRoll, Exterior1stAsphShn,
## Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: UtilitiesNoSeWa, Condition2PosA,
## Condition2RRNn, RoofMatlMetal, RoofMatlRoll, Exterior1stAsphShn,
## Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: UtilitiesNoSeWa, Condition2PosA,
## Condition2RRNn, RoofMatlMetal, RoofMatlRoll, Exterior1stAsphShn,
## Exterior1stImStucc## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, Exterior2ndOther, ExterCondPo,
## SaleTypeCon
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, Exterior2ndOther, ExterCondPo,
## SaleTypeCon
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, Exterior2ndOther, ExterCondPo,
## SaleTypeCon
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, Exterior2ndOther, ExterCondPo,
## SaleTypeCon
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, Exterior2ndOther, ExterCondPo,
## SaleTypeCon## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, FunctionalSev
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, FunctionalSev
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, FunctionalSev
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, FunctionalSev
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, FunctionalSev## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStuccpredictions.rf <- predict(rf, housing.test, na.action = na.pass)
RMSE(predictions.rf, housing.test$SalePrice)## [1] 26146.37varImp(rf)## rf variable importance
##
## only 20 most important variables shown (out of 274)
##
## Overall
## GrLivArea 100.00
## TotalBsmtSF 61.99
## X2ndFlrSF 56.96
## X1stFlrSF 52.07
## GarageArea 48.38
## LotArea 47.11
## YearBuilt 46.92
## YearRemodAdd 45.05
## GarageCars 45.01
## ExterQualTA 44.69
## Fireplaces 42.11
## BsmtFinSF1 40.47
## FireplaceQuNA 40.00
## GarageYrBlt 39.89
## OverallQual7 38.42
## MSZoningRL 37.41
## KitchenQualTA 37.12
## FullBath 36.76
## KitchenQualGd 36.59
## MSSubClass 36.55RMSE 26146.37
The variables: GrLivArea, TotalBsmtSF, X2ndFlrSF, X1stFlrSF, GarageArea, LotArea, YearBuilt, YearRemodAdd, GarageCars, and ExterQualTA were the 10 most predictive variables.
15.
set.seed(1)
gbm <- train(SalePrice ~ ., data = housing.train, preProc = "nzv", na.action = na.pass, method = "gbm", trControl = trainControl("cv", number = 10))## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 5810517523.4057 -nan 0.1000 412206625.2410
## 2 5418794652.7246 -nan 0.1000 367903339.1821
## 3 5101203039.4251 -nan 0.1000 282547983.2481
## 4 4776626098.1778 -nan 0.1000 301721161.9660
## 5 4477609190.4700 -nan 0.1000 284014279.7840
## 6 4231343318.6250 -nan 0.1000 195158781.9390
## 7 3977786668.2257 -nan 0.1000 234747904.8043
## 8 3759492265.4612 -nan 0.1000 222619710.9716
## 9 3579609849.8262 -nan 0.1000 179512399.0900
## 10 3419346423.7009 -nan 0.1000 160187693.9525
## 20 2313241552.3796 -nan 0.1000 79828744.0008
## 40 1433471318.6721 -nan 0.1000 18914487.5603
## 60 1143834913.8656 -nan 0.1000 -2658220.6605
## 80 1023183005.4850 -nan 0.1000 4306459.3543
## 100 957836892.5763 -nan 0.1000 -19801963.9460
## 120 901092450.9095 -nan 0.1000 -10308140.7067
## 140 865039959.0498 -nan 0.1000 -1054613.2748
## 150 856506078.4405 -nan 0.1000 -12190924.2521
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 5620611416.5482 -nan 0.1000 599917625.7505
## 2 5137946101.2975 -nan 0.1000 420877286.7205
## 3 4654762764.3019 -nan 0.1000 438202431.1373
## 4 4283144551.1734 -nan 0.1000 386876021.9633
## 5 3955196721.1058 -nan 0.1000 302784481.0819
## 6 3696440149.0543 -nan 0.1000 236531393.9556
## 7 3384204915.7867 -nan 0.1000 286696598.1948
## 8 3160573765.0286 -nan 0.1000 240312496.5710
## 9 2974748663.5895 -nan 0.1000 195376392.4608
## 10 2794410176.8200 -nan 0.1000 172435698.8097
## 20 1651427715.7795 -nan 0.1000 65014368.5059
## 40 1054662953.4832 -nan 0.1000 -3018825.9277
## 60 858099948.1010 -nan 0.1000 2912141.7942
## 80 769958965.2351 -nan 0.1000 -1122926.8329
## 100 696077517.5866 -nan 0.1000 -4848016.5611
## 120 646163156.1802 -nan 0.1000 1631823.8846
## 140 602423331.5376 -nan 0.1000 -3109313.5926
## 150 589343587.3863 -nan 0.1000 -9255723.4399
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 5517418278.0121 -nan 0.1000 729202838.5460
## 2 4955051879.9838 -nan 0.1000 519157110.7618
## 3 4473164300.1120 -nan 0.1000 521166451.0027
## 4 4047446208.6388 -nan 0.1000 403156289.8010
## 5 3660158899.4957 -nan 0.1000 339765601.1908
## 6 3347051597.7590 -nan 0.1000 252130820.0807
## 7 3050084479.5011 -nan 0.1000 248880501.1887
## 8 2818844839.5564 -nan 0.1000 231523845.5054
## 9 2594990838.3118 -nan 0.1000 196623509.9012
## 10 2402351294.7681 -nan 0.1000 166308496.8176
## 20 1355527258.4092 -nan 0.1000 39333664.4248
## 40 848960053.5965 -nan 0.1000 3531196.7250
## 60 691418996.7677 -nan 0.1000 -4176294.8259
## 80 608269528.1309 -nan 0.1000 -7369204.3735
## 100 534862826.0827 -nan 0.1000 -1684989.8913
## 120 493908562.0640 -nan 0.1000 -3374722.6912
## 140 446960423.2994 -nan 0.1000 -2784207.8908
## 150 430697169.1151 -nan 0.1000 -2987867.2184
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 6160592621.9610 -nan 0.1000 440980474.3094
## 2 5721062572.8025 -nan 0.1000 468108248.2007
## 3 5377056260.9103 -nan 0.1000 339473195.9387
## 4 5050393454.8901 -nan 0.1000 333211984.0022
## 5 4757841198.6609 -nan 0.1000 273548116.1285
## 6 4508356135.4049 -nan 0.1000 212047905.8927
## 7 4253928376.7478 -nan 0.1000 257245084.9928
## 8 4027555206.4180 -nan 0.1000 211904349.6546
## 9 3816184423.1873 -nan 0.1000 191675350.6181
## 10 3608336703.3204 -nan 0.1000 193272327.8169
## 20 2401865596.2038 -nan 0.1000 87088274.2661
## 40 1488204686.1044 -nan 0.1000 23197036.5846
## 60 1172838509.9474 -nan 0.1000 8260766.1687
## 80 1052457690.0236 -nan 0.1000 -1890633.7563
## 100 994905336.8291 -nan 0.1000 -6034837.7659
## 120 951020629.0980 -nan 0.1000 1114312.0158
## 140 915592506.9078 -nan 0.1000 -4679668.1222
## 150 897556229.4320 -nan 0.1000 -10875240.5056
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 5932896052.0099 -nan 0.1000 623229458.5710
## 2 5438051872.0562 -nan 0.1000 482736242.4847
## 3 4940288251.5179 -nan 0.1000 421052169.8095
## 4 4526453133.6632 -nan 0.1000 364627391.0810
## 5 4115946303.6636 -nan 0.1000 376356825.6445
## 6 3813151557.8601 -nan 0.1000 199197315.8465
## 7 3525982767.6679 -nan 0.1000 241109356.4354
## 8 3268871644.6728 -nan 0.1000 255921053.4491
## 9 3049402668.2721 -nan 0.1000 207305046.1762
## 10 2854540686.1239 -nan 0.1000 164955618.1738
## 20 1750638188.9392 -nan 0.1000 71096223.6568
## 40 1069532445.2089 -nan 0.1000 4550131.7524
## 60 894252109.4196 -nan 0.1000 -5056589.1692
## 80 802634388.9929 -nan 0.1000 -5314942.1957
## 100 748952348.1138 -nan 0.1000 1580061.7632
## 120 703082991.5817 -nan 0.1000 -6210867.3433
## 140 653207184.3743 -nan 0.1000 503726.2780
## 150 623916444.8743 -nan 0.1000 -10469170.8572
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 5873667976.7261 -nan 0.1000 699339729.0287
## 2 5257044053.8921 -nan 0.1000 562590660.3514
## 3 4759751201.2593 -nan 0.1000 459013496.7967
## 4 4314009656.3604 -nan 0.1000 495251224.8407
## 5 3918265084.7000 -nan 0.1000 398207708.1125
## 6 3556425515.6325 -nan 0.1000 279513907.7444
## 7 3248146267.6559 -nan 0.1000 256399912.2598
## 8 2988682621.7603 -nan 0.1000 166034171.0693
## 9 2748933634.1319 -nan 0.1000 217082741.8684
## 10 2542309929.0197 -nan 0.1000 179462099.7996
## 20 1419021454.6024 -nan 0.1000 39941999.6133
## 40 900892035.9553 -nan 0.1000 5280947.7033
## 60 742729530.6835 -nan 0.1000 -6376590.1733
## 80 650071632.5343 -nan 0.1000 6133130.1785
## 100 561974145.3448 -nan 0.1000 -3444218.8833
## 120 509443855.6271 -nan 0.1000 -4712274.7187
## 140 476684210.9352 -nan 0.1000 295956.3803
## 150 454116002.2801 -nan 0.1000 -3382557.4760
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 5854177546.9492 -nan 0.1000 411426808.7938
## 2 5467179066.5729 -nan 0.1000 367530678.1610
## 3 5099913719.5577 -nan 0.1000 356289670.6169
## 4 4812789684.2725 -nan 0.1000 297510644.9878
## 5 4532902023.6949 -nan 0.1000 282936660.3958
## 6 4274804392.8882 -nan 0.1000 252171188.3030
## 7 4050933350.4951 -nan 0.1000 213557624.9716
## 8 3795699047.6249 -nan 0.1000 205952599.3702
## 9 3613958853.4779 -nan 0.1000 173126567.8681
## 10 3437009008.1595 -nan 0.1000 167308480.5086
## 20 2280141171.1667 -nan 0.1000 59833351.5156
## 40 1380929195.7622 -nan 0.1000 3541053.1667
## 60 1077191987.2629 -nan 0.1000 7673119.9089
## 80 945945198.4697 -nan 0.1000 -2722636.5633
## 100 863146245.0508 -nan 0.1000 -8336866.7437
## 120 826777446.6781 -nan 0.1000 -9047415.1484
## 140 795933753.3884 -nan 0.1000 -4302703.0416
## 150 785176765.9823 -nan 0.1000 372388.4895
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 5736187185.9915 -nan 0.1000 471496778.8048
## 2 5138000641.2040 -nan 0.1000 605224389.1727
## 3 4642704103.5073 -nan 0.1000 523150625.1409
## 4 4222691961.0090 -nan 0.1000 416731612.2015
## 5 3849239919.7670 -nan 0.1000 333097803.9065
## 6 3494388893.9883 -nan 0.1000 324989727.3128
## 7 3225052644.6825 -nan 0.1000 252948456.8457
## 8 3005787386.3780 -nan 0.1000 209779818.0370
## 9 2810557141.2837 -nan 0.1000 189266161.6836
## 10 2612047504.4781 -nan 0.1000 191810167.5283
## 20 1545720657.3578 -nan 0.1000 55883087.8347
## 40 964693270.3917 -nan 0.1000 9798297.3384
## 60 811668771.7798 -nan 0.1000 -241604.2514
## 80 728811876.3073 -nan 0.1000 -3817861.6901
## 100 657856228.5770 -nan 0.1000 -4206200.7686
## 120 611141389.2523 -nan 0.1000 -2774582.1833
## 140 579147083.7213 -nan 0.1000 -4326920.1865
## 150 560774037.5304 -nan 0.1000 -676352.9107
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 5593116286.3290 -nan 0.1000 569468951.4793
## 2 5038768828.0325 -nan 0.1000 551580478.6502
## 3 4509650236.2430 -nan 0.1000 485479726.8933
## 4 4055592436.0314 -nan 0.1000 482690617.0540
## 5 3644329456.9311 -nan 0.1000 332883479.5129
## 6 3301305332.8843 -nan 0.1000 303295454.7224
## 7 3050909196.8196 -nan 0.1000 205657237.1342
## 8 2790592673.6670 -nan 0.1000 232997090.9615
## 9 2572328527.4820 -nan 0.1000 203848592.9195
## 10 2373081782.9094 -nan 0.1000 190721377.0404
## 20 1304881170.2175 -nan 0.1000 43886477.7512
## 40 781296089.3811 -nan 0.1000 4009959.1289
## 60 649796140.9502 -nan 0.1000 -10433171.3192
## 80 573744533.2247 -nan 0.1000 -5846268.9742
## 100 528855676.1426 -nan 0.1000 -3702813.5419
## 120 491809702.2441 -nan 0.1000 -4310375.2433
## 140 468101622.4865 -nan 0.1000 -3100111.2709
## 150 448695219.6123 -nan 0.1000 -4086275.7459
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 5847341678.8806 -nan 0.1000 421720505.1755
## 2 5452788520.8510 -nan 0.1000 430409585.2569
## 3 5119826590.5870 -nan 0.1000 342900553.2067
## 4 4806151223.9645 -nan 0.1000 268503305.8967
## 5 4530039305.8279 -nan 0.1000 247894363.5592
## 6 4262365253.1509 -nan 0.1000 253327629.5427
## 7 4043287335.2893 -nan 0.1000 210438761.6338
## 8 3813074071.0217 -nan 0.1000 214160571.9842
## 9 3632046358.5677 -nan 0.1000 181978421.6231
## 10 3469624686.1961 -nan 0.1000 168242020.7860
## 20 2337394428.9587 -nan 0.1000 67901921.1504
## 40 1483985205.7337 -nan 0.1000 22898100.9435
## 60 1183536372.2427 -nan 0.1000 278394.4829
## 80 1059742621.1655 -nan 0.1000 -8166767.4641
## 100 992871734.0824 -nan 0.1000 -6287229.8913
## 120 943305822.6502 -nan 0.1000 -7174664.3368
## 140 898931086.0325 -nan 0.1000 132999.9586
## 150 881799802.9681 -nan 0.1000 1577581.1737
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 5627890743.0495 -nan 0.1000 662855129.2384
## 2 5144482338.1345 -nan 0.1000 477226554.1946
## 3 4716882828.3830 -nan 0.1000 393360596.6879
## 4 4384393764.2846 -nan 0.1000 258086653.6198
## 5 4042772821.8348 -nan 0.1000 313252427.0590
## 6 3680050041.0097 -nan 0.1000 297280001.8267
## 7 3427412222.3900 -nan 0.1000 164387337.7731
## 8 3174057338.9701 -nan 0.1000 217509651.0387
## 9 2957871581.9347 -nan 0.1000 178625766.3336
## 10 2781334688.0406 -nan 0.1000 154878100.0353
## 20 1627830372.0761 -nan 0.1000 60664375.6110
## 40 1059486881.0296 -nan 0.1000 2399794.1625
## 60 884896419.7912 -nan 0.1000 -6273153.7402
## 80 781338792.0094 -nan 0.1000 -4460274.1577
## 100 721297604.7092 -nan 0.1000 -6256385.4306
## 120 666567910.2838 -nan 0.1000 -2355688.5252
## 140 624707924.5357 -nan 0.1000 -3073769.5811
## 150 608266028.2253 -nan 0.1000 -2934234.4090
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 5592830553.3143 -nan 0.1000 655593167.8272
## 2 5007981719.9299 -nan 0.1000 591094740.8248
## 3 4521829360.3523 -nan 0.1000 405214014.1913
## 4 4119851576.8613 -nan 0.1000 382786912.1430
## 5 3733827078.0281 -nan 0.1000 386668128.0735
## 6 3396530984.0230 -nan 0.1000 317129271.1247
## 7 3100977467.2646 -nan 0.1000 290515522.6069
## 8 2826817100.9276 -nan 0.1000 163894689.3521
## 9 2622409965.5217 -nan 0.1000 168871313.3858
## 10 2427795908.1872 -nan 0.1000 174375875.2713
## 20 1370651142.8774 -nan 0.1000 49013270.9160
## 40 831009793.9885 -nan 0.1000 5772235.6090
## 60 690239311.5191 -nan 0.1000 -4925737.2273
## 80 606994504.5978 -nan 0.1000 912882.8359
## 100 540071846.1607 -nan 0.1000 -2483141.5081
## 120 485988092.0815 -nan 0.1000 -1671109.9916
## 140 450962745.8178 -nan 0.1000 -3186404.0482
## 150 431364062.1999 -nan 0.1000 -484786.7896
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 5913759504.1483 -nan 0.1000 379316549.4356
## 2 5532270197.9776 -nan 0.1000 346078481.9455
## 3 5176062580.9699 -nan 0.1000 337816217.7217
## 4 4850335816.3248 -nan 0.1000 310247732.6538
## 5 4561926814.1061 -nan 0.1000 286554598.5713
## 6 4285720037.4924 -nan 0.1000 265121434.5288
## 7 4065401455.6381 -nan 0.1000 186523332.4169
## 8 3849159915.3790 -nan 0.1000 201998823.1491
## 9 3651364627.3071 -nan 0.1000 178330139.4247
## 10 3471790358.6992 -nan 0.1000 167343959.0756
## 20 2335119064.1154 -nan 0.1000 57546000.1150
## 40 1490729902.7350 -nan 0.1000 14413224.5154
## 60 1193541623.2322 -nan 0.1000 1516321.0805
## 80 1081165111.9317 -nan 0.1000 4564851.1559
## 100 1009774288.0583 -nan 0.1000 -4814511.8660
## 120 972984578.8250 -nan 0.1000 -10186891.7982
## 140 937479690.4936 -nan 0.1000 -4152431.1814
## 150 923495522.6108 -nan 0.1000 1601959.2728
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 5770247497.6126 -nan 0.1000 631822780.8018
## 2 5232692592.3193 -nan 0.1000 569469574.7582
## 3 4756476555.9040 -nan 0.1000 451361847.8192
## 4 4331646557.5298 -nan 0.1000 448707521.2263
## 5 4022739862.1484 -nan 0.1000 316396278.0784
## 6 3691672362.9062 -nan 0.1000 315651678.7719
## 7 3430416882.0506 -nan 0.1000 201383846.0448
## 8 3193170850.6813 -nan 0.1000 223174168.9583
## 9 2958787661.3042 -nan 0.1000 198501427.2016
## 10 2779420917.1476 -nan 0.1000 185447238.7936
## 20 1663709597.3424 -nan 0.1000 62927072.8980
## 40 1074476172.0298 -nan 0.1000 -193662.8415
## 60 905458131.0277 -nan 0.1000 -14006322.5989
## 80 808665487.9780 -nan 0.1000 1255365.9629
## 100 737975677.6894 -nan 0.1000 -8784102.0592
## 120 685017592.9054 -nan 0.1000 -6380446.8780
## 140 641859995.4666 -nan 0.1000 -4809223.3637
## 150 620769988.9924 -nan 0.1000 414814.8601
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 5670737667.8657 -nan 0.1000 704548445.2034
## 2 5079687418.9972 -nan 0.1000 542647052.0319
## 3 4573343679.8666 -nan 0.1000 437282872.7705
## 4 4113164494.6911 -nan 0.1000 359661430.9168
## 5 3730486995.9999 -nan 0.1000 368221395.7069
## 6 3398266144.2765 -nan 0.1000 344089466.4962
## 7 3116286670.8807 -nan 0.1000 217654013.5156
## 8 2863788181.6559 -nan 0.1000 250560033.0567
## 9 2641457178.3974 -nan 0.1000 211675714.6667
## 10 2444113564.4301 -nan 0.1000 144445221.1099
## 20 1422821734.9350 -nan 0.1000 37775664.1424
## 40 902576920.4174 -nan 0.1000 -3260084.0248
## 60 754277027.4505 -nan 0.1000 -1635629.2838
## 80 652767126.3607 -nan 0.1000 -2587996.6289
## 100 596812140.8238 -nan 0.1000 -10268817.7717
## 120 542640820.4174 -nan 0.1000 -3771407.3513
## 140 496612492.6886 -nan 0.1000 -2379011.9998
## 150 476510569.4942 -nan 0.1000 -317929.2953
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 6015688539.3074 -nan 0.1000 434265283.5254
## 2 5662518644.9517 -nan 0.1000 366728999.0463
## 3 5297257505.7971 -nan 0.1000 300188742.8196
## 4 4957064801.9814 -nan 0.1000 327682490.1510
## 5 4651993370.3552 -nan 0.1000 271131069.5595
## 6 4419584543.4236 -nan 0.1000 229538119.8002
## 7 4157505136.7809 -nan 0.1000 200658671.4111
## 8 3948808718.5255 -nan 0.1000 177832178.4891
## 9 3756103417.3924 -nan 0.1000 176266111.3316
## 10 3574418887.4538 -nan 0.1000 108067123.9794
## 20 2392537603.4594 -nan 0.1000 88514060.8708
## 40 1538723936.0438 -nan 0.1000 21317859.1992
## 60 1256640513.4725 -nan 0.1000 -167548.6880
## 80 1118773089.2890 -nan 0.1000 -6813971.3273
## 100 1039411120.9378 -nan 0.1000 3616882.3391
## 120 984784994.4457 -nan 0.1000 -1187358.5186
## 140 954194805.0250 -nan 0.1000 -8960840.7418
## 150 934512978.6260 -nan 0.1000 1401906.6821
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 5839659865.2026 -nan 0.1000 490458306.3422
## 2 5301059301.6303 -nan 0.1000 532753125.2908
## 3 4849270828.8471 -nan 0.1000 403486035.3662
## 4 4425399005.5279 -nan 0.1000 378739007.7588
## 5 4059700879.2591 -nan 0.1000 345901739.8822
## 6 3765759319.8551 -nan 0.1000 237332054.7883
## 7 3515833259.5746 -nan 0.1000 234823899.1492
## 8 3276872813.8491 -nan 0.1000 207786658.7914
## 9 3055575599.4047 -nan 0.1000 220367647.5010
## 10 2872473713.3813 -nan 0.1000 207452922.1768
## 20 1769385707.5505 -nan 0.1000 45397211.2705
## 40 1138157828.8653 -nan 0.1000 -10819644.6710
## 60 941681762.1068 -nan 0.1000 -11134083.1547
## 80 836598045.8169 -nan 0.1000 -10212782.3586
## 100 770765291.4753 -nan 0.1000 -3205441.4567
## 120 722089745.2672 -nan 0.1000 -4445965.9502
## 140 677819016.6476 -nan 0.1000 -2868324.7505
## 150 654195730.3485 -nan 0.1000 -7908829.5296
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 5782347524.3524 -nan 0.1000 650371793.5030
## 2 5188760023.5454 -nan 0.1000 540162142.6247
## 3 4713246559.4597 -nan 0.1000 497062567.0849
## 4 4272201009.9585 -nan 0.1000 388093685.1760
## 5 3903510795.4591 -nan 0.1000 359290013.2179
## 6 3564321318.9448 -nan 0.1000 309023707.7738
## 7 3300242795.0271 -nan 0.1000 295073842.6950
## 8 3044960145.2357 -nan 0.1000 223666961.6002
## 9 2804246967.6814 -nan 0.1000 203915922.4443
## 10 2589975872.5504 -nan 0.1000 206975992.2099
## 20 1470214007.9728 -nan 0.1000 42065900.6618
## 40 911763318.4862 -nan 0.1000 5080460.0301
## 60 742436942.7321 -nan 0.1000 -7643676.0366
## 80 642616565.1088 -nan 0.1000 -412982.9438
## 100 579358598.2890 -nan 0.1000 -3876573.5789
## 120 527323069.0771 -nan 0.1000 -2074107.0070
## 140 492295900.1968 -nan 0.1000 -3597403.4363
## 150 475041718.5944 -nan 0.1000 -4638598.0746
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 6003148545.5368 -nan 0.1000 440696197.7289
## 2 5618121806.2035 -nan 0.1000 366392849.6558
## 3 5224170672.5765 -nan 0.1000 361658686.2444
## 4 4933495569.9515 -nan 0.1000 305137426.3268
## 5 4631715251.3577 -nan 0.1000 276440603.2351
## 6 4340043716.7738 -nan 0.1000 225547108.1422
## 7 4090404820.5020 -nan 0.1000 226030029.3024
## 8 3872455818.5956 -nan 0.1000 183804381.4153
## 9 3676126741.8468 -nan 0.1000 141746170.3819
## 10 3508445556.3533 -nan 0.1000 158987616.0645
## 20 2367956137.9017 -nan 0.1000 86031425.4321
## 40 1502597310.7965 -nan 0.1000 4115433.5098
## 60 1220187489.2123 -nan 0.1000 -1318855.8802
## 80 1074970595.5351 -nan 0.1000 2965579.1358
## 100 1003656791.3753 -nan 0.1000 -8189501.8325
## 120 956755558.6838 -nan 0.1000 -8869984.2367
## 140 919918348.0513 -nan 0.1000 -1579839.9326
## 150 898859411.7137 -nan 0.1000 -4704408.8991
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 5768901813.5977 -nan 0.1000 627836519.7216
## 2 5240718046.4360 -nan 0.1000 423647901.9623
## 3 4775577372.0024 -nan 0.1000 446770169.5178
## 4 4387879994.8199 -nan 0.1000 285394394.8184
## 5 4075233364.2288 -nan 0.1000 216130282.7237
## 6 3795796524.8102 -nan 0.1000 267112607.3085
## 7 3512444381.9844 -nan 0.1000 279801897.1429
## 8 3258677866.8500 -nan 0.1000 256250438.3751
## 9 3052571649.2398 -nan 0.1000 181297324.5578
## 10 2839268670.9120 -nan 0.1000 211453440.1922
## 20 1736846741.0322 -nan 0.1000 54105253.7643
## 40 1078248341.3843 -nan 0.1000 12140673.3381
## 60 883645746.0234 -nan 0.1000 -1112589.9424
## 80 781860330.1106 -nan 0.1000 -11862966.2966
## 100 711181316.4385 -nan 0.1000 -7837990.6921
## 120 665628406.0639 -nan 0.1000 -4395166.4222
## 140 629399141.0209 -nan 0.1000 -148440.3852
## 150 610542717.6065 -nan 0.1000 -4788577.0379
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 5751620320.6718 -nan 0.1000 668215597.6215
## 2 5172206585.5886 -nan 0.1000 572925904.5185
## 3 4613787246.1843 -nan 0.1000 422444950.0094
## 4 4143842913.5124 -nan 0.1000 354858762.0584
## 5 3795927438.5854 -nan 0.1000 275781532.8229
## 6 3460142347.0227 -nan 0.1000 331748850.7943
## 7 3142590800.8974 -nan 0.1000 292610081.8179
## 8 2888256912.7457 -nan 0.1000 262227875.9217
## 9 2646521013.4902 -nan 0.1000 198088913.6044
## 10 2451612515.9980 -nan 0.1000 161215385.1770
## 20 1410538397.6859 -nan 0.1000 37892830.2839
## 40 886171624.8191 -nan 0.1000 3670007.1991
## 60 714804969.7231 -nan 0.1000 1162957.6605
## 80 619517452.3084 -nan 0.1000 -2792700.8311
## 100 557144561.1797 -nan 0.1000 -8024288.7576
## 120 515360424.7610 -nan 0.1000 -1765547.8045
## 140 472260135.5726 -nan 0.1000 -6271271.9655
## 150 459948745.0299 -nan 0.1000 -4140900.9787
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 6080116818.3424 -nan 0.1000 462920689.0670
## 2 5718627412.4956 -nan 0.1000 340208619.3661
## 3 5353777084.4551 -nan 0.1000 329792436.3414
## 4 4977977569.7284 -nan 0.1000 325165952.1541
## 5 4692671413.8403 -nan 0.1000 280814083.1291
## 6 4439333091.5729 -nan 0.1000 252488911.6995
## 7 4189011279.8147 -nan 0.1000 214073269.5163
## 8 4014023372.0433 -nan 0.1000 113463105.9833
## 9 3813272883.6062 -nan 0.1000 178236166.4268
## 10 3612898933.2786 -nan 0.1000 206562383.0715
## 20 2394397251.5079 -nan 0.1000 69368400.2742
## 40 1530528226.6767 -nan 0.1000 20628485.3485
## 60 1233394964.4762 -nan 0.1000 9586055.8782
## 80 1112242799.6090 -nan 0.1000 2302800.9602
## 100 1047893783.5159 -nan 0.1000 -1831676.5896
## 120 1006688677.0669 -nan 0.1000 -6209632.4181
## 140 966772817.9699 -nan 0.1000 -2076187.0415
## 150 948974224.1447 -nan 0.1000 -14630314.7414
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 6000303943.1096 -nan 0.1000 498509054.7949
## 2 5376986731.3652 -nan 0.1000 510378216.5721
## 3 4870500824.1644 -nan 0.1000 465081351.4042
## 4 4441362633.5819 -nan 0.1000 419688864.3220
## 5 4126222050.0349 -nan 0.1000 325721949.7884
## 6 3798763454.5292 -nan 0.1000 334162576.9759
## 7 3510550629.0862 -nan 0.1000 269901878.7145
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## 9 3068517127.4103 -nan 0.1000 158272906.0957
## 10 2867877635.7615 -nan 0.1000 158275084.7516
## 20 1738240305.3569 -nan 0.1000 53032923.6129
## 40 1096773559.7424 -nan 0.1000 8361598.5860
## 60 902202816.7975 -nan 0.1000 -487130.1552
## 80 811443122.5498 -nan 0.1000 -5710798.6224
## 100 750146070.9596 -nan 0.1000 -3887814.5939
## 120 698502911.8275 -nan 0.1000 -2116330.5754
## 140 656902967.7003 -nan 0.1000 -7356327.6513
## 150 645092435.8899 -nan 0.1000 -6239445.1527
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 5814572603.9377 -nan 0.1000 682996268.0045
## 2 5213534772.4058 -nan 0.1000 572722404.0068
## 3 4743232100.7752 -nan 0.1000 470016063.9899
## 4 4294117151.1999 -nan 0.1000 415934918.0683
## 5 3910970757.4528 -nan 0.1000 331536640.0875
## 6 3577315944.6631 -nan 0.1000 281135117.4293
## 7 3252368921.0338 -nan 0.1000 308897848.9231
## 8 3013621233.5182 -nan 0.1000 234735703.0026
## 9 2770442415.7970 -nan 0.1000 152555629.7149
## 10 2567408965.2898 -nan 0.1000 188505963.2604
## 20 1490052134.8872 -nan 0.1000 51235170.4764
## 40 941078347.0439 -nan 0.1000 5047527.5306
## 60 751221053.4070 -nan 0.1000 -1097530.2578
## 80 651469291.7804 -nan 0.1000 -2877321.3116
## 100 578342292.1491 -nan 0.1000 -4677671.7035
## 120 525325253.0438 -nan 0.1000 -6571431.5277
## 140 471453662.3423 -nan 0.1000 -2006252.6860
## 150 452214807.5770 -nan 0.1000 -3386295.7265
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 6172391947.1586 -nan 0.1000 411251054.4800
## 2 5696752229.9187 -nan 0.1000 498314328.1433
## 3 5353960938.9204 -nan 0.1000 354395476.8698
## 4 5013856954.8120 -nan 0.1000 338913713.3296
## 5 4728720431.4199 -nan 0.1000 281302437.6111
## 6 4468686282.4851 -nan 0.1000 271283423.3398
## 7 4235272267.8623 -nan 0.1000 231675675.8983
## 8 3995703447.1697 -nan 0.1000 181956116.2806
## 9 3790370007.0756 -nan 0.1000 198755178.0477
## 10 3625980449.6807 -nan 0.1000 166023793.1293
## 20 2394866616.2821 -nan 0.1000 77271672.1161
## 40 1463836946.9903 -nan 0.1000 20682126.5241
## 60 1156694466.0220 -nan 0.1000 1420630.2039
## 80 1014288959.2176 -nan 0.1000 885104.5064
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## 120 889811254.3538 -nan 0.1000 1459481.5101
## 140 858074617.4842 -nan 0.1000 -15133067.9541
## 150 842502225.8202 -nan 0.1000 -3928169.7886
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 5918363044.9584 -nan 0.1000 604381049.2106
## 2 5340248804.3755 -nan 0.1000 581720908.3032
## 3 4895092281.8385 -nan 0.1000 406324332.4276
## 4 4461739100.7458 -nan 0.1000 438131659.1260
## 5 4102085056.4324 -nan 0.1000 300179518.1686
## 6 3790136107.8580 -nan 0.1000 329946149.6718
## 7 3502219197.3189 -nan 0.1000 233046990.6349
## 8 3256201067.0370 -nan 0.1000 264607312.3862
## 9 3052512400.6930 -nan 0.1000 193738204.1323
## 10 2899921588.9719 -nan 0.1000 150857090.4569
## 20 1706513726.9130 -nan 0.1000 35975629.2356
## 40 1025444246.1755 -nan 0.1000 7174803.0967
## 60 837626927.1366 -nan 0.1000 -5065240.8924
## 80 743978793.2639 -nan 0.1000 -3337734.9498
## 100 690480223.0380 -nan 0.1000 -6589558.9514
## 120 650742869.3526 -nan 0.1000 -304704.1013
## 140 614344436.8564 -nan 0.1000 -2811297.0323
## 150 589405228.4681 -nan 0.1000 -2292717.2929
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 5804095752.8832 -nan 0.1000 730078416.8591
## 2 5225333786.1990 -nan 0.1000 540583549.1362
## 3 4637499707.5971 -nan 0.1000 462097826.6053
## 4 4190912982.2407 -nan 0.1000 460754355.6754
## 5 3815183057.7995 -nan 0.1000 280118980.6605
## 6 3478613702.9520 -nan 0.1000 316255786.6623
## 7 3160329054.1042 -nan 0.1000 319265728.0363
## 8 2919382571.0227 -nan 0.1000 209883445.6096
## 9 2681588574.3558 -nan 0.1000 232438953.5199
## 10 2480951527.0838 -nan 0.1000 176110026.0942
## 20 1382604245.3122 -nan 0.1000 55003823.6733
## 40 833902816.3544 -nan 0.1000 -3002335.7228
## 60 698815354.7706 -nan 0.1000 -3164541.8754
## 80 621440036.0748 -nan 0.1000 -3151055.9603
## 100 564508924.9198 -nan 0.1000 -8837790.2598
## 120 518215403.4279 -nan 0.1000 -4684376.8726
## 140 479049672.3819 -nan 0.1000 -913742.0487
## 150 463186574.5090 -nan 0.1000 -4875529.4769
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 6147920783.5987 -nan 0.1000 391818554.0390
## 2 5759701488.9908 -nan 0.1000 388088078.7301
## 3 5429352474.1258 -nan 0.1000 336602931.4662
## 4 5105764841.1366 -nan 0.1000 315930903.3408
## 5 4800440118.1854 -nan 0.1000 269569945.2893
## 6 4510229782.1048 -nan 0.1000 266597076.1856
## 7 4239577892.6229 -nan 0.1000 173261508.5707
## 8 3984619003.5126 -nan 0.1000 219104150.9707
## 9 3788076680.6935 -nan 0.1000 185147265.7937
## 10 3599482730.9720 -nan 0.1000 171931242.5041
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## 40 1486476659.3824 -nan 0.1000 19573351.3521
## 60 1215872495.5116 -nan 0.1000 6866562.7921
## 80 1112553238.7515 -nan 0.1000 1493051.3460
## 100 1043944153.8973 -nan 0.1000 1767901.7213
## 120 996959742.2375 -nan 0.1000 1628283.7121
## 140 972127411.2428 -nan 0.1000 -4253596.4131
## 150 953063130.9157 -nan 0.1000 -2739642.2835
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 5925009546.4218 -nan 0.1000 569711228.5859
## 2 5407797685.2981 -nan 0.1000 453672869.4688
## 3 4929804061.7367 -nan 0.1000 473961247.6789
## 4 4478379504.1601 -nan 0.1000 427565595.6868
## 5 4107730474.4833 -nan 0.1000 356419809.4052
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## 20 1719976487.2362 -nan 0.1000 70107244.9089
## 40 1123534925.1787 -nan 0.1000 7349205.0850
## 60 945818162.2569 -nan 0.1000 -9355518.9578
## 80 852515151.1640 -nan 0.1000 -11083434.9810
## 100 782075353.1237 -nan 0.1000 -1755176.9620
## 120 723801490.7290 -nan 0.1000 -691429.9723
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 5896175321.6603 -nan 0.1000 672929273.3563
## 2 5263758919.3340 -nan 0.1000 612364381.3973
## 3 4761268907.6126 -nan 0.1000 462411702.5635
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 5717854958.6248 -nan 0.1000 647384629.2667
## 2 5135196344.5461 -nan 0.1000 561323851.6643
## 3 4639991106.2062 -nan 0.1000 482440421.8882
## 4 4237903719.5836 -nan 0.1000 416519229.2280
## 5 3874573747.4656 -nan 0.1000 269917643.7163
## 6 3523440740.1211 -nan 0.1000 325327052.4296
## 7 3205670333.5018 -nan 0.1000 310694170.7426
## 8 2950904221.2388 -nan 0.1000 175817302.7315
## 9 2689527803.6894 -nan 0.1000 222614049.2454
## 10 2492069747.8062 -nan 0.1000 150621241.2042
## 20 1417436674.6797 -nan 0.1000 46224893.3319
## 40 897393789.3868 -nan 0.1000 -2797134.2295
## 60 736500028.2566 -nan 0.1000 1938194.0520
## 80 647526948.3527 -nan 0.1000 -4429868.8365
## 100 596957994.3258 -nan 0.1000 -896000.7551
## 120 547302725.9249 -nan 0.1000 -3157075.0850
## 140 505460775.8940 -nan 0.1000 -2596057.2899
## 150 487750801.8779 -nan 0.1000 -8510460.8780predictions.gbm <- predict(gbm, housing.test, na.action = na.pass)
RMSE(predictions.gbm, housing.test$SalePrice)## [1] 26147.59RMSE 26147.59
16.
set.seed(1)
svmlin <- train(SalePrice ~ ., data= housing.train, preProc = "knnImpute", na.action = na.pass, method = "svmLinear", trControl = trainControl("cv", number = 10), tuneGrid = expand.grid(C = c(1, 190, 225)))## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stCBlock, Exterior1stImStucc, Exterior2ndCBlock,
## ElectricalMix, MiscFeatureTenC## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stCBlock, Exterior1stImStucc, Exterior2ndCBlock,
## ElectricalMix, MiscFeatureTenC## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stCBlock, Exterior1stImStucc, Exterior2ndCBlock,
## ElectricalMix, MiscFeatureTenC## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlMembran,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc, Exterior1stStone## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlMembran,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc, Exterior1stStone## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlMembran,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc, Exterior1stStone## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition1RRNe, Condition2PosN,
## Condition2RRNn, RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition1RRNe, Condition2PosN,
## Condition2RRNn, RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition1RRNe, Condition2PosN,
## Condition2RRNn, RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAe, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAe, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAe, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, HeatingQCPo## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, HeatingQCPo## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, HeatingQCPo## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAn, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAn, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAn, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: UtilitiesNoSeWa, Condition2PosA,
## Condition2RRNn, RoofMatlMetal, RoofMatlRoll, Exterior1stAsphShn,
## Exterior1stImStucc## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: UtilitiesNoSeWa, Condition2PosA,
## Condition2RRNn, RoofMatlMetal, RoofMatlRoll, Exterior1stAsphShn,
## Exterior1stImStucc## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: UtilitiesNoSeWa, Condition2PosA,
## Condition2RRNn, RoofMatlMetal, RoofMatlRoll, Exterior1stAsphShn,
## Exterior1stImStucc## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, Exterior2ndOther, ExterCondPo,
## SaleTypeCon## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, Exterior2ndOther, ExterCondPo,
## SaleTypeCon## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, Exterior2ndOther, ExterCondPo,
## SaleTypeCon## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, FunctionalSev## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, FunctionalSev## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, FunctionalSev## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.predictions.svmlin <- predict(svmlin, housing.test, na.action = na.pass)
RMSE(predictions.svmlin, housing.test$SalePrice)## [1] 26994.74RMSE = 26994.74 C controls how big the penalty there is for the “soft margin” larger value = thinner margins.
17.
set.seed(1)
svmrad <- train(SalePrice ~ ., data= housing.train, preProc = "knnImpute", na.action = na.pass, method = "svmRadial", trControl = trainControl("cv", number = 10))## Warning in preProcess.default(method = "knnImpute", k = 5, x = structure(c(60, :
## These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stCBlock, Exterior1stImStucc, Exterior2ndCBlock,
## ElectricalMix, MiscFeatureTenC## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stCBlock, Exterior1stImStucc, Exterior2ndCBlock,
## ElectricalMix, MiscFeatureTenC## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stCBlock, Exterior1stImStucc, Exterior2ndCBlock,
## ElectricalMix, MiscFeatureTenC## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlMembran,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc, Exterior1stStone## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlMembran,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc, Exterior1stStone## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlMembran,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc, Exterior1stStone## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition1RRNe, Condition2PosN,
## Condition2RRNn, RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition1RRNe, Condition2PosN,
## Condition2RRNn, RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition1RRNe, Condition2PosN,
## Condition2RRNn, RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAe, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAe, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAe, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, HeatingQCPo## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, HeatingQCPo## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, HeatingQCPo## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAn, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAn, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAn, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: UtilitiesNoSeWa, Condition2PosA,
## Condition2RRNn, RoofMatlMetal, RoofMatlRoll, Exterior1stAsphShn,
## Exterior1stImStucc## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: UtilitiesNoSeWa, Condition2PosA,
## Condition2RRNn, RoofMatlMetal, RoofMatlRoll, Exterior1stAsphShn,
## Exterior1stImStucc## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: UtilitiesNoSeWa, Condition2PosA,
## Condition2RRNn, RoofMatlMetal, RoofMatlRoll, Exterior1stAsphShn,
## Exterior1stImStucc## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, Exterior2ndOther, ExterCondPo,
## SaleTypeCon## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, Exterior2ndOther, ExterCondPo,
## SaleTypeCon## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, Exterior2ndOther, ExterCondPo,
## SaleTypeCon## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, FunctionalSev## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, FunctionalSev## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, FunctionalSev## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.predictions.svmrad <- predict(svmrad, housing.test, na.action = na.pass)
RMSE(predictions.svmrad, housing.test$SalePrice)## [1] 77423.92RMSE = 77423.92
18.
compare = resamples(list(L=lasso, R=ridge, E=enet, RF=rf, svmLIN=svmlin, svmRAD=svmrad, G=gbm))
summary(compare, metric=compare$metrics)##
## Call:
## summary.resamples(object = compare, metric = compare$metrics)
##
## Models: L, R, E, RF, svmLIN, svmRAD, G
## Number of resamples: 10
##
## MAE
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## L 14701.66 16660.84 18364.14 18250.12 20141.77 21956.57 0
## R 15137.68 18273.20 19050.04 18941.71 20914.96 21819.43 0
## E 15137.68 18273.20 19050.04 18941.71 20914.96 21819.43 0
## RF 16072.94 16645.57 17794.08 17965.57 19094.18 20601.00 0
## svmLIN 13841.20 15698.66 16950.14 17046.90 18694.84 20207.97 0
## svmRAD 50827.34 53920.22 57266.55 55874.01 58091.90 59037.47 0
## G 15872.90 18882.69 19504.85 19386.12 20439.28 22806.77 0
##
## RMSE
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## L 20591.06 24655.50 29433.59 34129.47 37582.84 73690.11 0
## R 21174.89 26115.57 30206.80 34026.59 36962.98 68921.75 0
## E 21174.89 26115.57 30206.80 34026.59 36962.98 68921.75 0
## RF 22847.42 25335.14 28865.13 30461.72 34569.96 43917.08 0
## svmLIN 19190.40 22656.05 26584.85 30830.04 32740.14 63000.04 0
## svmRAD 72099.78 73747.36 81362.70 81830.12 89588.79 94405.31 0
## G 21963.15 25560.58 31139.04 31238.51 35515.43 45284.12 0
##
## Rsquared
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## L 0.4493449 0.8126273 0.8847586 0.8246095 0.8929485 0.9293024 0
## R 0.4817790 0.8201216 0.8739816 0.8256416 0.8841331 0.9314466 0
## E 0.4817790 0.8201216 0.8739816 0.8256416 0.8841331 0.9314466 0
## RF 0.7532677 0.8562373 0.8856585 0.8632701 0.9022848 0.9202865 0
## svmLIN 0.4946516 0.8797603 0.8949264 0.8528318 0.9197926 0.9420518 0
## svmRAD 0.2436634 0.4214664 0.4600261 0.4326348 0.4890375 0.5270952 0
## G 0.7421612 0.8386198 0.8650415 0.8495071 0.8947391 0.9078028 0Random forest had the best RMSE, but it was a narrow victory over the GBM model and the SVMLinear model.
19.
set.seed(123)
in_train <- createDataPartition(housing.train$SalePrice, p = .90, list = FALSE)
train <- housing.train[in_train, ]
val <- housing.train[-in_train, ]20.
library("RANN")
preproc <- preProcess(train, method="knnImpute")
train.imputed <- predict(preproc, train)
test.imputed <- predict(preproc, housing.test)
val.imputed <- predict(preproc, val)21.
library(mltools)##
## Attaching package: 'mltools'## The following object is masked from 'package:tidyr':
##
## replace_nalibrary(data.table)
train.onehot <- as.data.frame(one_hot(as.data.table(train.imputed), dropCols = TRUE, dropUnusedLevels = FALSE))
val.onehot <- as.data.frame(one_hot(as.data.table(val.imputed), dropCols = TRUE, dropUnusedLevels = FALSE))
test <- as.data.frame(one_hot(as.data.table(test.imputed), dropCols = TRUE, dropUnusedLevels = FALSE))
train.onehot <- train.onehot[ , -which(names(train.onehot) %in% "SalePrice")]
val.onehot <- val.onehot[ , -which(names(val.onehot) %in% "SalePrice")]
test <- test[ , -which(names(test) %in% "SalePrice")]
train.labels <- log(train$SalePrice)
val.labels <- log(val$SalePrice)
test_labels <- log(housing.test$SalePrice)22.
library(tfruns)
library(keras)
set.seed(1)
tensorflow::set_random_seed(1)## Loaded Tensorflow version 2.8.0housing_runs <- tuning_run("housing_tuning.R",
flags = list(
nodes = c(32, 64, 128, 392),
learning_rate = c(0.01, 0.05, 0.001, 0.0001),
batch_size=c(50, 100, 500, 1000),
epochs=c(30, 50, 100, 200),
activation=c("relu","sigmoid","tanh"),
dropout1=c(.2, .3, .5),
dropout2=c(.2, .4, .5)
), sample = .02)## 6,912 total combinations of flags## (sampled to 139 combinations)## Training run 1/139 (flags = list(32, 0.001, 1000, 200, "relu", 0.3, 0.2))## Using run directory runs/2022-04-19T14-22-55Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-22-55Z## Training run 2/139 (flags = list(128, 0.05, 500, 100, "relu", 0.2, 0.5))## Using run directory runs/2022-04-19T14-23-17Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-23-17Z## Training run 3/139 (flags = list(32, 0.01, 50, 100, "tanh", 0.5, 0.2))## Using run directory runs/2022-04-19T14-23-34Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-23-34Z## Training run 4/139 (flags = list(64, 0.01, 500, 100, "sigmoid", 0.2, 0.5))## Using run directory runs/2022-04-19T14-24-03Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-24-03Z## Training run 5/139 (flags = list(32, 1e-04, 1000, 200, "tanh", 0.3, 0.2))## Using run directory runs/2022-04-19T14-24-19Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-24-19Z## Training run 6/139 (flags = list(128, 0.05, 100, 200, "tanh", 0.5, 0.4))## Using run directory runs/2022-04-19T14-24-39Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-24-39Z## Training run 7/139 (flags = list(128, 0.001, 500, 30, "relu", 0.2, 0.4))## Using run directory runs/2022-04-19T14-25-06Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-25-06Z## Training run 8/139 (flags = list(64, 1e-04, 50, 30, "sigmoid", 0.2, 0.2))## Using run directory runs/2022-04-19T14-25-17Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-25-17Z## Training run 9/139 (flags = list(64, 0.01, 100, 200, "relu", 0.5, 0.4))## Using run directory runs/2022-04-19T14-25-30Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-25-30Z## Training run 10/139 (flags = list(128, 0.001, 1000, 100, "tanh", 0.2, 0.5))## Using run directory runs/2022-04-19T14-25-56Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-25-56Z## Training run 11/139 (flags = list(128, 0.01, 1000, 30, "sigmoid", 0.3, 0.4))## Using run directory runs/2022-04-19T14-26-16Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-26-16Z## Training run 12/139 (flags = list(32, 0.01, 500, 200, "relu", 0.5, 0.4))## Using run directory runs/2022-04-19T14-26-29Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-26-29Z## Training run 13/139 (flags = list(32, 0.05, 100, 50, "tanh", 0.2, 0.2))## Using run directory runs/2022-04-19T14-26-51Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-26-51Z## Training run 14/139 (flags = list(32, 0.05, 100, 30, "tanh", 0.3, 0.2))## Using run directory runs/2022-04-19T14-27-05Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-27-05Z## Training run 15/139 (flags = list(64, 0.05, 1000, 100, "tanh", 0.3, 0.5))## Using run directory runs/2022-04-19T14-27-17Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-27-17Z## Training run 16/139 (flags = list(64, 0.001, 50, 50, "sigmoid", 0.2, 0.2))## Using run directory runs/2022-04-19T14-27-32Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-27-32Z## Training run 17/139 (flags = list(128, 0.05, 50, 30, "sigmoid", 0.5, 0.2))## Using run directory runs/2022-04-19T14-27-47Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-27-47Z## Training run 18/139 (flags = list(32, 0.001, 50, 50, "relu", 0.5, 0.4))## Using run directory runs/2022-04-19T14-28-00Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-28-00Z## Training run 19/139 (flags = list(128, 1e-04, 50, 50, "sigmoid", 0.3, 0.5))## Using run directory runs/2022-04-19T14-28-15Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-28-15Z## Training run 20/139 (flags = list(32, 0.05, 100, 200, "relu", 0.5, 0.2))## Using run directory runs/2022-04-19T14-28-31Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-28-31Z## Training run 21/139 (flags = list(32, 0.05, 500, 30, "relu", 0.2, 0.2))## Using run directory runs/2022-04-19T14-28-57Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-28-57Z## Training run 22/139 (flags = list(32, 0.001, 500, 50, "sigmoid", 0.3, 0.2))## Using run directory runs/2022-04-19T14-29-08Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-29-08Z## Training run 23/139 (flags = list(32, 0.001, 100, 200, "tanh", 0.2, 0.2))## Using run directory runs/2022-04-19T14-29-22Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-29-22Z## Training run 24/139 (flags = list(64, 1e-04, 500, 50, "relu", 0.3, 0.2))## Using run directory runs/2022-04-19T14-29-48Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-29-48Z## Training run 25/139 (flags = list(32, 0.05, 500, 200, "sigmoid", 0.2, 0.2))## Using run directory runs/2022-04-19T14-30-02Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-30-02Z## Training run 26/139 (flags = list(64, 0.01, 500, 30, "tanh", 0.3, 0.5))## Using run directory runs/2022-04-19T14-30-24Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-30-24Z## Training run 27/139 (flags = list(64, 1e-04, 1000, 50, "sigmoid", 0.5, 0.5))## Using run directory runs/2022-04-19T14-30-35Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-30-35Z## Training run 28/139 (flags = list(392, 0.001, 500, 100, "relu", 0.5, 0.4))## Using run directory runs/2022-04-19T14-30-48Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-30-48Z## Training run 29/139 (flags = list(128, 1e-04, 50, 200, "sigmoid", 0.2, 0.5))## Using run directory runs/2022-04-19T14-31-07Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-31-07Z## Training run 30/139 (flags = list(32, 0.01, 500, 30, "tanh", 0.2, 0.4))## Using run directory runs/2022-04-19T14-31-40Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-31-40Z## Training run 31/139 (flags = list(392, 0.01, 100, 50, "tanh", 0.2, 0.4))## Using run directory runs/2022-04-19T14-31-52Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-31-52Z## Training run 32/139 (flags = list(64, 0.05, 50, 50, "sigmoid", 0.5, 0.5))## Using run directory runs/2022-04-19T14-32-08Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-32-08Z## Training run 33/139 (flags = list(64, 0.001, 50, 50, "relu", 0.2, 0.4))## Using run directory runs/2022-04-19T14-32-23Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-32-23Z## Training run 34/139 (flags = list(64, 0.001, 500, 30, "relu", 0.2, 0.5))## Using run directory runs/2022-04-19T14-32-43Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-32-43Z## Training run 35/139 (flags = list(64, 0.05, 500, 100, "tanh", 0.3, 0.2))## Using run directory runs/2022-04-19T14-32-55Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-32-55Z## Training run 36/139 (flags = list(128, 1e-04, 500, 50, "tanh", 0.5, 0.2))## Using run directory runs/2022-04-19T14-33-11Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-33-11Z## Training run 37/139 (flags = list(392, 0.01, 100, 100, "sigmoid", 0.3, 0.4))## Using run directory runs/2022-04-19T14-33-26Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-33-26Z## Training run 38/139 (flags = list(392, 0.001, 100, 100, "sigmoid", 0.5, 0.2))## Using run directory runs/2022-04-19T14-33-56Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-33-56Z## Training run 39/139 (flags = list(392, 0.01, 100, 30, "sigmoid", 0.2, 0.4))## Using run directory runs/2022-04-19T14-34-26Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-34-26Z## Training run 40/139 (flags = list(64, 0.001, 1000, 200, "tanh", 0.3, 0.2))## Using run directory runs/2022-04-19T14-34-39Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-34-39Z## Training run 41/139 (flags = list(392, 0.001, 500, 30, "tanh", 0.5, 0.5))## Using run directory runs/2022-04-19T14-35-00Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-35-00Z## Training run 42/139 (flags = list(128, 0.05, 100, 50, "tanh", 0.5, 0.4))## Using run directory runs/2022-04-19T14-35-15Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-35-15Z## Training run 43/139 (flags = list(64, 0.05, 50, 50, "tanh", 0.5, 0.5))## Using run directory runs/2022-04-19T14-35-29Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-35-29Z## Training run 44/139 (flags = list(392, 0.05, 500, 30, "sigmoid", 0.5, 0.4))## Using run directory runs/2022-04-19T14-35-44Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-35-44Z## Training run 45/139 (flags = list(32, 0.001, 100, 30, "relu", 0.2, 0.5))## Using run directory runs/2022-04-19T14-35-59Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-35-59Z## Training run 46/139 (flags = list(128, 0.05, 1000, 50, "sigmoid", 0.5, 0.5))## Using run directory runs/2022-04-19T14-36-11Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-36-11Z## Training run 47/139 (flags = list(392, 0.05, 1000, 200, "sigmoid", 0.5, 0.4))## Using run directory runs/2022-04-19T14-36-26Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-36-26Z## Training run 48/139 (flags = list(64, 0.05, 50, 200, "sigmoid", 0.3, 0.2))## Using run directory runs/2022-04-19T14-36-54Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-36-54Z## Training run 49/139 (flags = list(64, 0.001, 1000, 50, "relu", 0.2, 0.4))## Using run directory runs/2022-04-19T14-37-44Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-37-44Z## Training run 50/139 (flags = list(128, 0.05, 500, 30, "tanh", 0.5, 0.2))## Using run directory runs/2022-04-19T14-37-58Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-37-58Z## Training run 51/139 (flags = list(128, 0.01, 1000, 100, "relu", 0.2, 0.4))## Using run directory runs/2022-04-19T14-38-10Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-38-10Z## Training run 52/139 (flags = list(64, 0.001, 500, 30, "tanh", 0.2, 0.4))## Using run directory runs/2022-04-19T14-38-27Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-38-27Z## Training run 53/139 (flags = list(64, 0.05, 50, 100, "relu", 0.3, 0.5))## Using run directory runs/2022-04-19T14-38-39Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-38-39Z## Training run 54/139 (flags = list(392, 0.05, 100, 30, "relu", 0.3, 0.5))## Using run directory runs/2022-04-19T14-39-09Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-39-09Z## Training run 55/139 (flags = list(392, 1e-04, 100, 100, "relu", 0.5, 0.2))## Using run directory runs/2022-04-19T14-39-23Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-39-23Z## Training run 56/139 (flags = list(64, 1e-04, 50, 30, "tanh", 0.2, 0.2))## Using run directory runs/2022-04-19T14-39-45Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-39-45Z## Training run 57/139 (flags = list(64, 0.01, 50, 100, "tanh", 0.5, 0.5))## Using run directory runs/2022-04-19T14-39-58Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-39-58Z## Training run 58/139 (flags = list(32, 1e-04, 500, 30, "sigmoid", 0.3, 0.2))## Using run directory runs/2022-04-19T14-40-19Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-40-19Z## Training run 59/139 (flags = list(64, 0.01, 100, 100, "relu", 0.3, 0.5))## Using run directory runs/2022-04-19T14-40-31Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-40-31Z## Training run 60/139 (flags = list(64, 0.05, 100, 30, "relu", 0.2, 0.2))## Using run directory runs/2022-04-19T14-40-50Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-40-50Z## Training run 61/139 (flags = list(64, 1e-04, 50, 200, "sigmoid", 0.3, 0.5))## Using run directory runs/2022-04-19T14-41-02Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-41-02Z## Training run 62/139 (flags = list(64, 0.05, 500, 200, "sigmoid", 0.3, 0.5))## Using run directory runs/2022-04-19T14-41-35Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-41-35Z## Training run 63/139 (flags = list(32, 0.01, 50, 200, "relu", 0.5, 0.5))## Using run directory runs/2022-04-19T14-42-05Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-42-05Z## Training run 64/139 (flags = list(128, 0.01, 1000, 50, "relu", 0.3, 0.5))## Using run directory runs/2022-04-19T14-42-37Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-42-37Z## Training run 65/139 (flags = list(128, 0.01, 1000, 200, "sigmoid", 0.5, 0.5))## Using run directory runs/2022-04-19T14-42-51Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-42-51Z## Training run 66/139 (flags = list(392, 0.05, 500, 50, "sigmoid", 0.3, 0.2))## Using run directory runs/2022-04-19T14-43-14Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-43-14Z## Training run 67/139 (flags = list(128, 0.05, 100, 200, "relu", 0.3, 0.2))## Using run directory runs/2022-04-19T14-43-28Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-43-28Z## Training run 68/139 (flags = list(128, 1e-04, 1000, 200, "relu", 0.5, 0.2))## Using run directory runs/2022-04-19T14-43-57Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-43-57Z## Training run 69/139 (flags = list(392, 0.001, 500, 200, "relu", 0.3, 0.5))## Using run directory runs/2022-04-19T14-44-20Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-44-20Z## Training run 70/139 (flags = list(64, 0.05, 50, 50, "relu", 0.5, 0.4))## Using run directory runs/2022-04-19T14-44-49Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-44-49Z## Training run 71/139 (flags = list(392, 0.05, 500, 50, "sigmoid", 0.3, 0.5))## Using run directory runs/2022-04-19T14-45-05Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-45-05Z## Training run 72/139 (flags = list(128, 0.05, 500, 50, "relu", 0.5, 0.2))## Using run directory runs/2022-04-19T14-45-20Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-45-20Z## Training run 73/139 (flags = list(128, 0.001, 50, 50, "sigmoid", 0.2, 0.5))## Using run directory runs/2022-04-19T14-45-34Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-45-34Z## Training run 74/139 (flags = list(32, 0.01, 100, 200, "sigmoid", 0.2, 0.2))## Using run directory runs/2022-04-19T14-45-50Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-45-50Z## Training run 75/139 (flags = list(392, 0.01, 500, 200, "sigmoid", 0.2, 0.5))## Using run directory runs/2022-04-19T14-46-17Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-46-17Z## Training run 76/139 (flags = list(32, 1e-04, 50, 30, "sigmoid", 0.3, 0.5))## Using run directory runs/2022-04-19T14-46-46Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-46-46Z## Training run 77/139 (flags = list(392, 1e-04, 500, 100, "sigmoid", 0.3, 0.2))## Using run directory runs/2022-04-19T14-47-01Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-47-01Z## Training run 78/139 (flags = list(128, 0.05, 100, 30, "relu", 0.5, 0.4))## Using run directory runs/2022-04-19T14-47-20Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-47-20Z## Training run 79/139 (flags = list(64, 1e-04, 500, 50, "sigmoid", 0.3, 0.2))## Using run directory runs/2022-04-19T14-47-33Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-47-33Z## Training run 80/139 (flags = list(392, 0.01, 100, 50, "relu", 0.2, 0.2))## Using run directory runs/2022-04-19T14-47-46Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-47-46Z## Training run 81/139 (flags = list(128, 0.05, 500, 50, "sigmoid", 0.5, 0.2))## Using run directory runs/2022-04-19T14-48-07Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-48-07Z## Training run 82/139 (flags = list(32, 1e-04, 100, 30, "relu", 0.3, 0.4))## Using run directory runs/2022-04-19T14-48-21Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-48-21Z## Training run 83/139 (flags = list(32, 1e-04, 50, 100, "tanh", 0.2, 0.5))## Using run directory runs/2022-04-19T14-48-34Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-48-34Z## Training run 84/139 (flags = list(392, 0.001, 1000, 200, "tanh", 0.5, 0.2))## Using run directory runs/2022-04-19T14-49-04Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-49-04Z## Training run 85/139 (flags = list(64, 0.05, 100, 100, "relu", 0.5, 0.4))## Using run directory runs/2022-04-19T14-49-35Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-49-35Z## Training run 86/139 (flags = list(128, 0.001, 100, 200, "tanh", 0.5, 0.5))## Using run directory runs/2022-04-19T14-49-55Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-49-55Z## Training run 87/139 (flags = list(392, 0.001, 500, 50, "tanh", 0.5, 0.5))## Using run directory runs/2022-04-19T14-50-24Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-50-24Z## Training run 88/139 (flags = list(392, 1e-04, 500, 30, "tanh", 0.3, 0.2))## Using run directory runs/2022-04-19T14-50-39Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-50-39Z## Training run 89/139 (flags = list(32, 1e-04, 500, 30, "tanh", 0.2, 0.2))## Using run directory runs/2022-04-19T14-50-52Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-50-52Z## Training run 90/139 (flags = list(32, 0.05, 100, 100, "sigmoid", 0.3, 0.5))## Using run directory runs/2022-04-19T14-51-04Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-51-04Z## Training run 91/139 (flags = list(128, 1e-04, 100, 30, "sigmoid", 0.2, 0.2))## Using run directory runs/2022-04-19T14-51-22Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-51-22Z## Training run 92/139 (flags = list(64, 0.05, 500, 50, "tanh", 0.5, 0.5))## Using run directory runs/2022-04-19T14-51-35Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-51-35Z## Training run 93/139 (flags = list(32, 0.01, 100, 100, "relu", 0.3, 0.4))## Using run directory runs/2022-04-19T14-51-47Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-51-47Z## Training run 94/139 (flags = list(32, 0.001, 50, 50, "sigmoid", 0.5, 0.5))## Using run directory runs/2022-04-19T14-52-05Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-52-05Z## Training run 95/139 (flags = list(128, 0.05, 500, 200, "sigmoid", 0.5, 0.5))## Using run directory runs/2022-04-19T14-52-20Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-52-20Z## Training run 96/139 (flags = list(64, 0.05, 1000, 50, "tanh", 0.3, 0.4))## Using run directory runs/2022-04-19T14-52-50Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-52-50Z## Training run 97/139 (flags = list(64, 0.01, 1000, 200, "tanh", 0.3, 0.2))## Using run directory runs/2022-04-19T14-53-05Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-53-05Z## Training run 98/139 (flags = list(64, 0.001, 100, 50, "relu", 0.3, 0.2))## Using run directory runs/2022-04-19T14-53-27Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-53-27Z## Training run 99/139 (flags = list(392, 1e-04, 500, 30, "tanh", 0.3, 0.5))## Using run directory runs/2022-04-19T14-53-41Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-53-41Z## Training run 100/139 (flags = list(128, 0.001, 500, 50, "tanh", 0.5, 0.5))## Using run directory runs/2022-04-19T14-53-57Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-53-57Z## Training run 101/139 (flags = list(392, 1e-04, 1000, 30, "relu", 0.2, 0.5))## Using run directory runs/2022-04-19T14-54-10Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-54-10Z## Training run 102/139 (flags = list(64, 1e-04, 100, 200, "relu", 0.3, 0.2))## Using run directory runs/2022-04-19T14-54-26Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-54-26Z## Training run 103/139 (flags = list(128, 0.05, 500, 50, "relu", 0.3, 0.4))## Using run directory runs/2022-04-19T14-54-53Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-54-53Z## Training run 104/139 (flags = list(392, 0.001, 1000, 30, "sigmoid", 0.2, 0.2))## Using run directory runs/2022-04-19T14-55-08Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-55-08Z## Training run 105/139 (flags = list(128, 0.01, 1000, 30, "tanh", 0.2, 0.5))## Using run directory runs/2022-04-19T14-55-23Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-55-23Z## Training run 106/139 (flags = list(32, 1e-04, 100, 200, "tanh", 0.2, 0.2))## Using run directory runs/2022-04-19T14-55-34Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-55-34Z## Training run 107/139 (flags = list(64, 0.01, 500, 30, "relu", 0.3, 0.5))## Using run directory runs/2022-04-19T14-56-01Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-56-01Z## Training run 108/139 (flags = list(64, 0.001, 50, 100, "tanh", 0.5, 0.5))## Using run directory runs/2022-04-19T14-56-13Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-56-13Z## Training run 109/139 (flags = list(32, 0.01, 50, 100, "relu", 0.5, 0.5))## Using run directory runs/2022-04-19T14-56-36Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-56-36Z## Training run 110/139 (flags = list(128, 0.001, 500, 30, "sigmoid", 0.2, 0.5))## Using run directory runs/2022-04-19T14-57-06Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-57-06Z## Training run 111/139 (flags = list(128, 0.001, 1000, 100, "sigmoid", 0.2, 0.5))## Using run directory runs/2022-04-19T14-57-18Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-57-18Z## Training run 112/139 (flags = list(64, 0.001, 100, 30, "relu", 0.2, 0.4))## Using run directory runs/2022-04-19T14-57-35Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-57-35Z## Training run 113/139 (flags = list(128, 1e-04, 50, 100, "sigmoid", 0.3, 0.2))## Using run directory runs/2022-04-19T14-57-48Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-57-48Z## Training run 114/139 (flags = list(64, 0.001, 1000, 100, "sigmoid", 0.3, 0.4))## Using run directory runs/2022-04-19T14-58-10Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-58-10Z## Training run 115/139 (flags = list(32, 1e-04, 100, 30, "sigmoid", 0.5, 0.5))## Using run directory runs/2022-04-19T14-58-26Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-58-26Z## Training run 116/139 (flags = list(392, 0.05, 100, 100, "relu", 0.5, 0.4))## Using run directory runs/2022-04-19T14-58-39Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-58-39Z## Training run 117/139 (flags = list(64, 0.001, 500, 100, "relu", 0.5, 0.2))## Using run directory runs/2022-04-19T14-59-01Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-59-01Z## Training run 118/139 (flags = list(32, 0.05, 1000, 200, "sigmoid", 0.2, 0.2))## Using run directory runs/2022-04-19T14-59-18Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-59-18Z## Training run 119/139 (flags = list(392, 0.001, 50, 200, "tanh", 0.3, 0.4))## Using run directory runs/2022-04-19T14-59-49Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T14-59-49Z## Training run 120/139 (flags = list(128, 1e-04, 1000, 200, "sigmoid", 0.5, 0.5))## Using run directory runs/2022-04-19T15-00-31Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T15-00-31Z## Training run 121/139 (flags = list(392, 0.05, 100, 30, "tanh", 0.2, 0.2))## Using run directory runs/2022-04-19T15-00-53Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T15-00-53Z## Training run 122/139 (flags = list(392, 0.001, 100, 200, "sigmoid", 0.2, 0.5))## Using run directory runs/2022-04-19T15-01-06Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T15-01-06Z## Training run 123/139 (flags = list(64, 0.05, 100, 200, "relu", 0.3, 0.4))## Using run directory runs/2022-04-19T15-01-57Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T15-01-57Z## Training run 124/139 (flags = list(128, 0.01, 500, 100, "tanh", 0.3, 0.4))## Using run directory runs/2022-04-19T15-02-23Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T15-02-23Z## Training run 125/139 (flags = list(128, 0.001, 500, 30, "tanh", 0.2, 0.5))## Using run directory runs/2022-04-19T15-02-40Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T15-02-40Z## Training run 126/139 (flags = list(32, 0.01, 50, 30, "tanh", 0.2, 0.5))## Using run directory runs/2022-04-19T15-02-51Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T15-02-51Z## Training run 127/139 (flags = list(32, 1e-04, 100, 30, "relu", 0.2, 0.2))## Using run directory runs/2022-04-19T15-03-04Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T15-03-04Z## Training run 128/139 (flags = list(64, 1e-04, 50, 50, "tanh", 0.3, 0.4))## Using run directory runs/2022-04-19T15-03-17Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T15-03-17Z## Training run 129/139 (flags = list(64, 0.05, 100, 100, "sigmoid", 0.5, 0.2))## Using run directory runs/2022-04-19T15-03-32Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T15-03-32Z## Training run 130/139 (flags = list(64, 0.05, 50, 50, "sigmoid", 0.2, 0.5))## Using run directory runs/2022-04-19T15-03-51Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T15-03-51Z## Training run 131/139 (flags = list(392, 0.001, 100, 30, "sigmoid", 0.5, 0.2))## Using run directory runs/2022-04-19T15-04-11Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T15-04-11Z## Training run 132/139 (flags = list(392, 0.01, 1000, 50, "sigmoid", 0.3, 0.5))## Using run directory runs/2022-04-19T15-04-25Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T15-04-25Z## Training run 133/139 (flags = list(32, 0.001, 500, 200, "tanh", 0.5, 0.2))## Using run directory runs/2022-04-19T15-04-40Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T15-04-40Z## Training run 134/139 (flags = list(32, 0.05, 500, 30, "tanh", 0.3, 0.2))## Using run directory runs/2022-04-19T15-05-10Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T15-05-10Z## Training run 135/139 (flags = list(32, 1e-04, 1000, 30, "relu", 0.2, 0.5))## Using run directory runs/2022-04-19T15-05-22Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T15-05-22Z## Training run 136/139 (flags = list(64, 1e-04, 500, 100, "sigmoid", 0.5, 0.2))## Using run directory runs/2022-04-19T15-05-33Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T15-05-33Z## Training run 137/139 (flags = list(32, 0.01, 1000, 50, "sigmoid", 0.2, 0.2))## Using run directory runs/2022-04-19T15-05-50Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T15-05-50Z## Training run 138/139 (flags = list(128, 0.01, 50, 200, "relu", 0.2, 0.4))## Using run directory runs/2022-04-19T15-06-04Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T15-06-04Z## Training run 139/139 (flags = list(64, 0.05, 100, 50, "sigmoid", 0.5, 0.4))## Using run directory runs/2022-04-19T15-06-39Z##
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size",
## + 100), flag_string("activation", "relu"), flag_numeric("learning_rate", .... [TRUNCATED]
##
## > model = keras_model_sequential()
##
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation,
## + input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED]
##
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
## + loss = "mse", metrics = "mae")
##
## > model %>% fit(as.matrix(train.onehot), train.labels,
## + epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]##
## Run completed: runs/2022-04-19T15-06-39Z23.
housing_runs_ordered <- housing_runs[order(housing_runs$metric_val_loss), ]
head(housing_runs_ordered)## Data frame: 6 x 27
## run_dir metric_loss metric_mae metric_val_loss
## 2 runs/2022-04-19T15-06-04Z 0.1501 0.3062 0.0158
## 109 runs/2022-04-19T14-31-52Z 0.2980 0.4348 0.0159
## 101 runs/2022-04-19T14-34-26Z 0.5177 0.5669 0.0183
## 21 runs/2022-04-19T14-59-49Z 0.2772 0.4241 0.0198
## 102 runs/2022-04-19T14-33-56Z 0.2688 0.4159 0.0199
## 17 runs/2022-04-19T15-01-57Z 0.0395 0.1395 0.0207
## metric_val_mae
## 2 0.0924
## 109 0.0919
## 101 0.1006
## 21 0.1046
## 102 0.1064
## 17 0.1061
## # ... with 22 more columns:
## # flag_nodes, flag_batch_size, flag_activation, flag_learning_rate,
## # flag_epochs, flag_dropout1, flag_dropout2, epochs, epochs_completed,
## # metrics, model, loss_function, optimizer, learning_rate, script, start,
## # end, completed, output, source_code, context, typeview_run(housing_runs$run_dir[2])## starting httpd help server ... done## Warning in readLines(file.path(source_dir, file)): incomplete final line found
## on '/tmp/RtmpVCtGOJ/file3699f4655ea331/source/housing_tuning.R'The best model was run #2 with a val_loss of .0158. The model is a pretty good fit. Not excessively overfitting or underfitting. loss and validation loss appear to be decreasing and converging together in the graph.
The hyper parameters are: nodes = 128, batch_size = 50, activation = relu, learning rate = .01, epochs = 200, dropout1 = .2, dropout 2 = .4
24.
# combine train w/ validation
housing_train <- rbind(train.onehot, val.onehot)
housing_train_labels <- c(train.labels, val.labels)
set.seed(1)
tensorflow::set_random_seed(1)
best_model = keras_model_sequential()
best_model %>%
layer_dense(units = 128, activation = "relu", input_shape = ncol(housing_train)) %>%
layer_dropout(.2) %>%
layer_dense(units = 128, activation = "relu") %>%
layer_dropout(.4) %>%
layer_dense(units = 1)
best_model %>% compile(
optimizer = optimizer_adam(learning_rate=.01),
loss = 'mse',
metrics = 'mae')
best_model %>% fit(
as.matrix(housing_train), housing_train_labels, epochs = 200,
batch_size = 50, validation_data=list(as.matrix(test), test_labels))25.
predictions.nn <- best_model %>% predict(as.matrix(test))
RMSE(exp(predictions.nn), housing.test$SalePrice)## [1] 47067.6RMSE = 47067.6
26.
RMSE Comparison Lasso - 34113.53 Ridge - 32406.35 Elastic Net - 32406.35 Random Forest - 26146.37 GBM - 26147.59 svmLinear - 26994.74 svmRadial - 77423.92 Neural Network - 47067.6
The random forest model performed best on this dataset, but the GBM model was very close.